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C7020-230 | IBM Watson V3 Application Development

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C7020-230 - IBM Watson V3 Application Development - braindump

Vendor IBM
Exam Number C7020-230
Exam Name IBM Watson V3 Application Development
Questions 63 Q & A
Recent Update December 11, 2018
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C7020-230 exam Dumps Source : IBM Watson V3 Application Development

Test Code : C7020-230
Test Name : IBM Watson V3 Application Development
Vendor Name : IBM
Q&A : 63 Real Questions

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IBM IBM Watson V3 Application

IBM Says Blockchain Can power 'Open Scientific research' in New Patent filing | killexams.com Real Questions and Pass4sure dumps

A patent application posted Thursday claims the manner of conducting scientific analysis can advantage from the blockchain.

Led by using a team at IBM’s Watson research center, the patent application presents a vision for dynamic collaboration – one where researchers can music their work across institutional borders. It’s yet another non-financial software of the distributed-ledger technology, which IBM has championed in contemporary months.

This newest patent may also be idea of as an tricky software changelog, but for science. Or, because the filing places it, a system that gives “a tamper resistant log of scientific analysis.”

From the filing:

“The blockchain device can kind a blockchain representing a research task, wherein the blockchain contains a primary block of analysis facts and a second block of analysis statistics representing a log of an evaluation performed on the analysis facts. abstract blocks and correction blocks can even be brought to the blockchain representing the publish evaluation of the analysis consequences.”

The software – titled “Blockchain for Open Scientific research” – turned into first filed with the U.S. Patent and Trademark office in December 2017. IBM researchers Jae-wook Ahn, Maria Chang, Patrick Watson and Ravindranath Kokku are listed as inventors.

in accordance with the patent, “currently, there are restrained systems that permit for sharing information about scientific research and showing transparent statistics collection and analysis steps. platforms that do exist, lack the requisite controls and mechanisms to permit for trustworthy information, as there are few options for guaranteeing that statistics might be proof against modification.”

IBM isn’t the handiest community working to follow allotted ledger know-how to the scientific realm. A Berlin-primarily based believe tank, Blockchain for Science, held its first international convention prior this week.

The blockchain-flavored patent is certainly one of many for massive Blue. in response to information published in September, IBM turned into at the back of most effective chinese internet huge Alibaba within the variety of blockchain-related patent filings.

IBM graphic by way of Shutterstock


IBM Is taking place - however can also be Saved | killexams.com Real Questions and Pass4sure dumps

No result found, try new keyword!Most of its revenues come from infrastructure-related choices while the next income comes from application ... many Watson customers are pondering of scaling down. As you keep in mind, IBM beneath ...

IBM Watson: The inside story of how the Jeopardy-successful supercomputer became born, and what it desires to do next | killexams.com Real Questions and Pass4sure dumps

IBM Watson wowed the tech business and a corner of U.S. pop tradition with its 2011 win in opposition t two of Jeopardy's choicest champions. here's how IBM pulled it off and a look at what Watson's actual career goes to be.

Between them, they'd racked up over $5 million in winnings on the tv quiz exhibit Jeopardy. They have been the surest players the demonstrate had produced over its decades-lengthy lifetime: Ken Jennings had the longest unbeaten run at 74 successful appearances, while Brad Rutter had earned the largest prize pot with a complete of $three.25 million.

Rutter and Jennings had been Jeopardy-successful machines. And in early 2011, they agreed to an exhibition match towards an opponent who'd never even stood in the back of a Jeopardy podium before.

but this Jeopardy unknown had spent years making ready to take on both giants in the $1m match, playing one hundred video games against previous winners in order to enhance his probabilities of profitable.

That opponent failed to smile, provided all his solutions in the identical impassive tone, and would not sit down in the same room as his fellow contestants. He had to work too tough at conserving his cool and changed into so noisy, it turned into concept he become too disruptive to take the rostrum in adult. He become stored in a back room, his answers piped into the studio.

You wouldn't know through taking a look at him what he turned into considering - possibly you would spot just a tinge of coloration when he changed into confusing over a particularly hard question.

The contender begun out with a run of winning answers - he knew his Beatles songs, Olympic background, literary criminals. sure, he wasn't too conventional along with his Harry Potter, however he stretched out a lead however, leaving Rutter and Jennings trailing heaps of dollars behind.

however questions about decades tripped him up, and Rutter fought again, piling up adequate cash to unsettle any individual who'd wager on the influence of the fit. by using the conclusion of the first of the special exhibition suit shows, you'd had been complicated pushed to work out which turned into safest along with your cash.

however then Double Jeopardy happened. The upstart powered through the huge questions, winning even with guesses he changed into removed from satisfied about, and inserting extraordinary bets that got here decent.

by means of the end of the 2d episode, the unknown had $25,000 more than his closest opponent, Rutter. Rutter and Jennings looked increasingly uncomfortable as it begun to look like they'd get a pasting from the new boy, bobbing in frustration as their opponent buzzed in before them time and time once more.

"I, for one, welcome our new computing device overlords" Ken Jennings' response to dropping to an exhibition Jeopardy healthy to Watson

Jennings managed a late fightback in the third episode, but the new opponent progressively clawed back ample money to make it a close run.

All three accurately answered the last query 'William Wilkinson's 'An account of the principalities of Wallachia and Moldavia' impressed this creator's most noted novel' with 'who's Bram Stoker?' but Jennings appended his response with: "I for one welcome our new computer overlords".

He, and Rutter, had misplaced to Watson - a room-sized beast of a laptop made with the aid of IBM and named after the business's founder Thomas J Watson.

Watson, including ten racks of ten vigor 750 servers, had to be stored apart from the human contestants because of the roar of its cooling equipment and turned into represented on the podium via an avatar of IBM's Smarter Planet brand, whose moving traces would go eco-friendly when Watson had cracked a thorny difficulty, orange when the reply turned into wrong.

while Watson had the questions delivered in text instead of by way of being attentive to the quizmaster, he played the video game like his human counterparts: puzzle over the query, buzz in, supply the answer it really is absolutely to be correct, tot up some prize money.

And Watson become correct lots of the time. He received the game with $77,147 leaving Rutter and Jennings within the filth with $21,600 and $24,000 respectively.

It turned out that the true Jeopardy winning desktop become, smartly, a computer.

Three nights, two people, one computer and $1 million: the victory of IBM's Watson over two human contestants on Jeopardy turned into the primary, and possibly simplest, time the computer impressed itself on the well-known public's recognition.

watson-jeopardy

IBM Watson defeated two of Jeopardy's most advantageous champions.

graphic: IBM

however even earlier than Watson secured its now-noted win, IBM became working on a way to turn the adorable quiz demonstrate-dominating computer into a serious enterprise contender.

Watson begun lifestyles 5 years earlier than its television appearance, when IBM analysis execs were searching for the next "Grand challenge" for the company. IBM periodically runs these Grand Challenges, selected tasks that pit man towards computer, have overseas appeal, are handy to grasp and attract people into working in science and maths fields. along with Watson, the Grand Challenges have spawned Deep Blue, the computer that famously beat grand master Garry Kasparov at chess, and the Blue Gene supercomputer.

within the mid-2000s, IBM became on the lookout for its subsequent Grand challenge. Paul Horn, then director of IBM analysis, turned into in favour of attempting to increase a machine that might win the Turing verify, a means to measure computer intelligence via having a system try to fool a human into thinking that they are having a conversation with a further adult.

As challenging as passing the Turing check is - no desktop has yet completed it - it was felt that it would not perhaps gentle up the public's creativeness as different tasks had. however had been there any linked challenges that might nonetheless carry those facets of competing against people and figuring out human speech together?

"They originally spoke of no, or not it's a foolish task to work on, it be too gimmicky, it's now not a true computing device science examine, and we doubtless cannot do it anyway" IBM researchers' first tackle constructing a desktop that might win Jeopardy

"Beating a human in Jeopardy is a step in that course - the questions are advanced and nuanced, and it takes a special classification of computing device to have a chance of beating a human by answering these classification of questions. i used to be working the analysis division and i become bugging people in the organization, in certain [former EVP in IBM's software group] Charles Lickel," Horn stated.

Lickel was inspired to tackle the problem of constructing a Jeopardy-winning computing device after having dinner with his team. "We had been at a steak house in Fishtail, new york. within the core of dinner, abruptly the total restaurant cleared out to the bar - I turned to my group and requested 'what's going on?'. It turned into very ordinary. I hadn't in fact been following Jeopardy, but it surely became out it was when Ken Jennings was having his long winning streak, and everybody desired to discover if he would win once more that nighttime, and that they'd long gone to the bar to see," Lickel stated. Jennings received once again that nighttime, and nevertheless holds the longest unbeaten run on Jeopardy with seventy four appearances undefeated.

The thought of a quiz champion computing device failed to instantly win his team around, with many of Lickel's top of the line personnel asserting they failed to accept as true with a computing device may compete with, not to mention beat, flesh and blood champions.

"They initially observed no, it be a silly undertaking to work on, or not it's too gimmicky, it's not a true desktop science verify, and we likely cannot do it anyway," talked about Horn.

then again, a group sufficiently adventuresome to take on the challenge of constructing a Jeopardy winner become discovered.

It become nevertheless a small mission and recommendations of commercialisation weren't uppermost in any person's mind - Grand Challenges had been demonstration projects, whose return for the enterprise was extra in the buzz they created than in a contribution to the final analysis. If commercialisation took place, extremely good - however for now, Watson turned into simply slightly of a moonshot for IBM.

because of the preliminary measurement of the trouble, it changed into funded from the research group's normal price range and did not require signal-off from massive Blue's better-ups, which means it may function freed from the business pressures of most tasks.

Jeopardy's quirk is that instead of the quizmaster environment questions and contestants presenting the reply, the quizmaster gives the answers, known as 'clues' in Jeopardy-communicate, to which contestants deliver a question. not simplest would the desktop need to be capable of produce questions for the viable clues that might come its approach on Jeopardy, it will should be able to first pull apart Jeopardy's tricksy clues - work out what was being asked - before it might even provide the appropriate response.

trebeck-jeopardy

Jeopardy host Alex Trebeck and the IBM crew speak in regards to the exhibition suit with Watson.

photo: IBM

For that, IBM developed DeepQA, a hugely parallel application architecture that examined natural language content in both the clues set by means of Jeopardy and in Watson's own saved data, along with searching into the structured information it holds. The component-based mostly system, constructed on a series of pluggable components for looking and weighting guidance, took about 20 researchers three years to reach a degree where it may tackle a quiz exhibit performance and come out looking more desirable than its human opponents.

First up, DeepQA works out what the query is asking, then works out some viable solutions according to the suggestions it has to hand, creating a thread for each and every. each thread makes use of a whole bunch of algorithms to examine the facts, taking a look at components together with what the tips says, what category of tips it is, its reliability, and how possible it is to be imperative, then creating a person weighting in accordance with what Watson has previously realized about how probably they are to be appropriate. It then generated a ranked list of solutions, with proof for each of its options.

The guidance that DeepQA would eventually be able to question for Jeopardy changed into 200 million pages of advice, from a variety of sources. the entire suggestions had to be in the neighborhood kept - Watson wasn't allowed to hook up with the web all the way through the quiz - and understood, queried and processed at a good clip: in a Jeopardy's case, Watson had to spit out an answers in a matter of seconds to make sure it turned into first to the buzzer.

"when I left IBM in conclusion of 2007, Watson changed into an embryonic venture," observed Horn. "It had three americans in Charles Lickel's area that acquired the information from the old Jeopardy programmes and had been beginning to educate the computer. It could barely beat a five yr old at that time. The projection changed into 'god knows how long it would take to beat an grownup, let alone a grand champion'. Then over time when it appeared like they begun to have an opportunity, Dave below the management of John Kelly grew the task into something mammoth," stated Horn.

while there's still debate over precisely when the concept of making Watson pay its way eventually took shape at IBM, when Watson took to the stage for its Jeopardy-profitable efficiency, the display featured IBM professionals talking about viable makes use of for the equipment in healthcare, and strikes to set up a Watson enterprise unit began no longer lengthy after the Jeopardy reveal aired.

IBM's then-CEO Sam Palmisano and its existing CEO Ginni Rometty, under whose remit Watson fell on the time, all started discussions in the weeks after the win, and the task became moved from below the wing of IBM analysis and into the IBM application neighborhood.

In August of 2011, the Watson business unit correct came into being, headed up through Manoj Saxena, who'd joined IBM some years previous when the company he labored for, Webify, became bought by IBM.

Saxena changed into the unit's worker no 1. within three months, he had been joined with the aid of 107 new Watson staffers, generally technologists within the fields of herbal language processing and desktop gaining knowledge of.

Healthcare had already been recommended because the first business Watson should target for commercial choices, but there were no plans to confine it simply to drugs. Any assistance-intensive trade was reasonable video game, any place had been there have been big volumes of unstructured and semi-structured data that Watson could ingest, be aware and system faster than its human counterparts. Healthcare could be a starting point, however banking, assurance, and telecoms were all within the firing line.

but how do you turn a quiz reveal winner into some thing greater enterprise-like? First job for the Watson group become to get to grips with the laptop they'd inherited from IBM research, remember the forty one separate subsystems that went into Watson, and determine what mandatory to be mounted up earlier than Watson may placed on its go well with and tie.

within the Watson unit's first yr, the device obtained sped up and slimmed down. "We serialised the threads and how the utility worked and drove up the performance," Saxena said. "The device today in comparison to the Jeopardy equipment is about 240 p.c sooner and it's one-sixteenth the measurement. The equipment that was the size of a master bedroom will now run in a gadget the size of the vegetable drawer on your double-drawer fridge."

one more method of it: a single energy 750 server, measuring 9 inches high, 18 inches wide and 36 inches deep, and weighing in at around 100 kilos. Having got the device to a extra manageable measurement for companies, it set about finding valued clientele to take it on.

IBM had healthcare pegged as its first vertical for Watson from the time of the Jeopardy win. youngsters, while Jeopardy Watson and healthcare Watson share a common heritage, they may be distinct entities: IBM forked the Watson code for its industrial incarnation.

"The system that become the measurement of a grasp bedroom will now run in a equipment the measurement of the vegetable drawer to your double-drawer fridge" Watson VP Manoj Saxena on the shrinking Watson

Jeopardy Watson had one project - get an answer, take note it, and locate the query that went with it. It turned into a single person equipment - had three quizmasters put three answers to it, it might have thrown the computing device right into a spin. Watson had to be retooled for a state of affairs the place tens, hundreds, although many, clinicians can be asking questions directly, and never single questions either - complex dialog with several connected queries one after the different, all asked in non-general formats. And, of path, there become the English language itself with all its messy complexity.

"there have been primary areas of innovation that had to be finished to go past Jeopardy - there changed into an enormous volume of pre-processing, post-processing and tooling that we have delivered around the core engines," introduced Saxena. "it's the equal of getting a Ferrari engine then trying to construct an entire race automobile around it. What we inherited was the core engine, and we observed 'ok, let's construct a brand new issue that does all variety of things the common Jeopardy equipment wasn't required to do'."

To get Watson from Jeopardy to oncology, there have been three procedures that the Watson crew went through: content adaptation, practising adaptation, and practical adaptation - or, to position it one more manner, feeding it medical information and having it weighted accurately; trying out it out with some practice questions; then making any technical changes obligatory - tweaking taxonomies, for example.

The content adaptation for healthcare adopted the identical path as getting Watson as much as speed for the quiz display: feed it suggestions, show it what right appears like, then let it guess what right looks like and proper it if it's wrong. In Jeopardy, that supposed feeding it with hundreds of query and answer pairs from the display, after which demonstrating what a appropriate response gave the impression of. Then it became given just the solutions, and requested to get a hold of the questions. When it went wrong, it become corrected. via computer learning, it could begin to get a handle on this reply-query component, and adjust its algorithms consequently.

watson-on-phone

Watson has moved on to solutions that can vigor searches from smartphones. graphic: IBM

"it will be fed many circumstances the place the history became regular and correct remedy changed into familiar, and then, analogous to working towards for Jeopardy, or not it's been given instances after which it suggests treatments," Kohn said.

Some information came from what IBM describes as a Jeopardy-like video game known as medical professional's catch 22 situation, whose questions encompass 'the syndrome characterized through joint ache, stomach ache, palpable purpura, and a nephritic sediment?'. (The answer, of direction, is Henoch-Schönlein purpura.)

The working towards, says Kohn, "is an ongoing procedure, and Watson is abruptly enhancing its means to make comparatively cheap techniques the oncologists suppose are effective."

via 2012, there were two healthcare enterprises that had began piloting Watson.

Wellpoint, one of the US largest insurers, become one of the most pair of agencies that helped outline the application of Watson in health. The different become Memorial Sloan-Kettering cancer center (MSKCC), an organization IBM already had a relationship with and which is determined not far from each IBM's personal Armonk headquarters and the research laboratories in York Heights, manhattan that nonetheless residence the first Watson.

And it was this relationship that helped spur Watson's first industrial stream into working within the box of cancer treatment plans. while using Watson as a analysis device should be would becould very well be its most obvious software in healthcare, using it to help in selecting the right therapy for a cancer affected person made much more feel. MSKCC was a tertiary referral centre - by the point sufferers arrived, they already had their analysis.

So Watson was destined first to be an oncologist's assistant, digesting reams of facts - MSKCC's personal, scientific journals, articles, patients notes and greater - together with sufferers' preferences to come up with tips for treatment alternate options. every could be weighted hence, depending on how primary Watson calculated they had been.

unlike its Jeopardy counterpart, healthcare Watson additionally has the capability to go browsing - not all its records needs to be saved. And while Watson had two million pages of scientific information from 600,000 sources to swallow, it might nevertheless make use of the customary capabilities garnered for Jeopardy - particulars from Wikipedia, as an example. (What it does not use, besides the fact that children, is the urban Dictionary. Fed into Watson late remaining year, it turned into reportedly eliminated after answering a researcher's query with the notice "bullshit". "We did discover some exciting responses, so we had to shut that down," Saxena referred to diplomatically. "That isn't to be repeated, since it would be considered as very improper in definite instances, and we had to train Watson the appropriate company behaviour.")

watson-info-box

This chart, carried out a 12 months after Watson's Jeopardy win, suggests some of its speedy growth.

image: IBM

As such, the sources are actually medical publications like Nature and the British clinical Journal. And there are different safety nets too.

"in the teaching part, a doctor - a melanoma care professional during this case - sits down and asks questions of Watson and corrects the laptop learning. The medical professional and an information scientist are sitting subsequent to each and every other, correcting Watson. Spurious cloth, or conflicted cloth or whatever thing from a pharmaceutical company that the medical professional feels could be biased - this is caught all the way through the training cycle," delivered Saxena.

WellPoint and MSKCC used Watson as the foundation for techniques that might read and remember volumes of medical literature and different assistance - patients' treatment and family histories, as an example, as well as clinical trials and articles in scientific journals - to help oncologists through recommending lessons of treatment.

A yr of working with both firms has produced industrial products: Interactive Care Insights for Oncology, and the WellPoint Interactive Care e book and Interactive Care Reviewer. Interactive Care Insights for Oncology provides tips for medication plans for lung melanoma sufferers, whereas New WellPoint Interactive Care ebook and Interactive Care Reviewer experiences clinicians' recommended remedies against their sufferers' plans and is anticipated to be in use at 1,600 healthcare providers this year.

Watson has larger ambitions than a clinician's assistant, although. Its scientific knowledge is around that of a first yr scientific student, in keeping with IBM, and the business hopes to have Watson flow the familiar medical licensing board assessments within the not too distant future.

"Our work today is in the very early levels round practice of drugs, round chronic care ailments. We're starting with cancer and we are able to soon add diabetes, cardiology, mental fitness, different persistent diseases. after which our work is on the price side, where we are streamlining the authorisation and approval method between hospitals, clinics and coverage groups," Saxena pointed out.

The most efficient purpose for Watson is to be an aid to analysis - in place of simply suggesting treatments for melanoma, because it does nowadays, it may assist medical doctors in making a choice on the ailments that deliver americans to the clinics within the first place.

before then, there is work to be accomplished. whereas large records providers commonly trumpet the growth of unstructured statistics and the forsaking of relational databases, for Watson, it be these older sources of records that present extra of a problem.

"Watson works in particular with natural language - free text or textual content-like assistance - and that is the reason about 80 p.c of the big volumes of healthcare information attainable to us," mentioned Kohn. "Then there is the 20 percent this is structured data - truly, numerical facts - or photographs, like MRIs, CAT scans, and the like. Watson does not manner structured facts at once and it doesn't interpret pictures. it may interpret the report attached to a picture, however now not the photo itself."

additionally, IBM is working on making a broader healthcare offering on the way to take it beyond its oncology roots.

"notwithstanding Watson is working with these two establishments, what the designers and desktop scientists are focusing on [is] that some thing they improve is generalisable, it's no longer just niche for melanoma remedy and particularly for the a few cancers we're working with. We're the usage of it as a learning method to create algorithms and methodologies that might be without difficulty generalisable to any enviornment of healthcare. They wouldn't have to should say, right, we now have oncology below handle, now let's birth once again with family apply or cardiology," Kohn mentioned.

ibm-citiback-ebrain

Citi and IBM have been taking part on company methods seeing that the days of the early mainframes.

graphic: IBM

Watson has also already discovered some hobby in banking. Citi is the use of Watson to increase customer journey with the financial institution and create new services. or not it's convenient to see how Watson may well be put to make use of, say, identifying no matter if a borderline-chance company consumer is probably going to repay the loan they've applied for, or used to pick out cases of fraud or id theft before purchasers may well be conscious they're happening.

Citi continues to be early in its Watson experiments. A spokeswoman talked about the company is currently just "exploring use circumstances".

From right here on in, instead of being standalone items, the subsequent Watson choices to hit the market should be embedded into items within the IBM Smarter Planet product line. they're anticipated to appear in the second half of the year.

the first such Smarter Planet product regarded in may additionally: IBM Engagement advisor. The conception at the back of the Engagement advisor, aimed at contact centres, is that customer service brokers can query their employers' databases and different assistance sources using herbal language whereas they are conducting helpline conversations with their valued clientele. one of the vital groups checking out out the service is Australia's ANZ bank, the place it could be assisting name centre personnel with making economic capabilities techniques to individuals who ring up.

watson-question-phone

Ask Watson goes some distance beyond what Apple's Siri can do, IBM believes.

photograph: IBM

Watson could possibly one day scour purchasable proof for the foremost time to find someone in a position to speak and choose the communication channel most likely to generate a positive response, or pore over social media for disgruntled consumers and supply solutions to their problems in natural language.

There are also plans to alternate how Watson's delivered, too. instead of just interacting with it by way of a name centre worker, customers will soon be in a position to become familiar with the Engagement consultant. instead of have some name centre agent examine out Watson generated guidance to a client with, say, a fault with their new washing machine or a stock-trader trying advice on updating their portfolio, the client and dealer could just quiz Watson directly from their telephone or pill, by typing their query straight right into a company' app. Apps with Watson under the hood should be out in the latter half of this year, based on Forbes.

IBM pros have additionally in the past counseled that Watson could come to be a supercharged edition of Siri, where individuals can be capable of speak directly into their cell and pose a posh query for Watson to reply - a farmer protecting up his smartphone to take video of his fields, and asking Watson when to plant corn, as an instance.

IBM is eager to spell out the transformations between Watson and Siri. "Watson is aware of what it is aware of - and with the aid of listening, learning and the usage of human-like thinking capabilities uncovers insights from massive records, Watson also immediately ascertains what it doesn't know. Siri, nevertheless, quite simply appears for key words to go looking the web for lists of alternate options that it chooses one from," the enterprise says. however, the evaluation holds: Watson could certainly have a future as your infinitely a professional personal assistant.

whereas including voice-focus capabilities to Watson should be no exceptional shakes for IBM given its latest partnerships, such a movement would require Watson to be in a position to understand pictures (whatever IBM's already engaged on) that would require Watson to query all forms of sources of information including newspapers, books, photos, repositories of records that have been made publicly purchasable, social media and the cyber web at gigantic. That Watson should tackle such a job within the coming years, primarily if the processing goes on in an IBM datacentre and never on the cell itself, as you could possibly expect, is actually inside the nation-states of the viable.

As IBM seeks to embed Watson's capabilities into further and further products, how far does the company suppose Watson will unfold within the coming years? it's going to best say gnomically, "as we continue to scale our capabilities, we intend to make Watson available as a set of features in many industries." need a stronger answer? greater ask Watson.

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Building an IBM Watson bot with Node.js + Botkit | killexams.com real questions and Pass4sure dumps

Bots are fun and useful with new businesses being built daily around bot interaction models. We recently launched our new Team Messaging API — Glip - at Enterprise Connect and we’re excited to show off more of the capabilities of the platform we’re building at RingCentral, alongside with IBM Watson Bluemix and Botkit!

A step-by-step tutorial walking through the Github code is available here!

Prerequisites
  • Two free RingCentral Developer accounts
  • A free IBM Bluemix Developer account
  • Creating a new RC App

    Login to the RingCentral developer account to create a new app. Click on “Create App” which will create your app and auto-create a new sandbox account if you don’t already have one.

    Create App

    Enter the application name and description.

    Add Application Information

    There are two paths for development on the RingCentral platform: making a private app which is just for your team, or a public app for public distribution through our App Gallery that others can use.

    Application and Authorization flow

    In the OAuth settings, make sure the permission selected is Glip. This permission is required to build chatbots.

    OAuth Settings and Permissions

    Once the account has been created, you will be redirected to the dashboard. The App Key, App Secret, and the phone number will be used to play with the bot in the team. Save it for the next step.

    App Credentials Botkit Setup

    Botkit is one of the most popular open-source toolkits for building bots, providing the essential building blocks . Botkit supports multiple frameworks, but here we will be looking at Glip. Glip is currently in review process with Botkit but it can be install directly from Github. Bot can communicate with your team in three ways:

  • Incoming webhooks: is a simple way to post messages from external sources in Glip. e.g JIRA bots which post into your team when a new ticket has been assigned to you.
  • Slash Command: are custom commands written by developers like you. They start with a backslash (/) and act as a trigger event to perform some action. Slash commands are currently not available in Glip. They are scheduled to be available by end of year.
  • Real Time Messaging API: allows you to send and receive messages from a Glip user.
  • With some theory behind us, let’s start building our sample application. This example will focus on getting post messages from a group or team in Glip and pass the message to IBM Watson to gather some personality insight.

    What are Personality Insights ?

    According to IBM, “Personality Insights extracts personality characteristics based on how a person writes. You can use the service to match individuals to other individuals, opportunities, and products, or tailor their experience with personalized messaging and recommendations. Characteristics include the Big 5 Personality Traits, Values, and Needs. At least 1200 words of input text are recommended when using this service.”

    As a first step, clone the sample application from Github. Now create a copy of the .env.template file and rename it to .env. The file should look something like this:

    GLIP_SERVER=https://platform.ringcentral.comGLIP_APPKEY=GLIP_APPSECRET=GLIP_USERNAME=GLIP_PASSWORD=GLIP_EXTENSION=WATSON_USERNAME=WATSON_PASSWORD=WATSON_URL=https://gateway.watsonplatform.net/personality-insights/apiWATSON_VERSION_DATE=2016-10-19WATSON_VERSION=v3

    Then use the below code to install all dependencies including botkit and watson-developer-cloud.

    npm install --save

    Now let’s connect Botkit with Glip using the following code snippet:

    "use strict";

    require('dotenv').config();

    var Botkit= require('botkit'),os = require('os'),http = require('http'),request = require('request'),watson = require('watson-developer-cloud');

    var controller = Botkit.glipbot({debug: false,});

    var bot = controller.spawn({server: process.env.GLIP_SERVER,appKey: process.env.GLIP_APPKEY,appSecret: process.env.GLIP_APPSECRET,username: process.env.GLIP_USERNAME,password: process.env.GLIP_PASSWORD,extension: process.env.GLIP_EXTENSION,}).startRTM();

    controller.setupWebserver(process.env.port || 3000, function(err, webserver){webserver.get('/', function (req ,res) {res.send(':)');});

    controller.createWebhookEndpoints(webserver, bot);

    });

    controller.hears(['watson'], 'message_received', function (bot, message) {

    bot.reply(message, "hi, this is watson, How can I help you ? ");});

    In the above example, when someone mentions “watson” the bot responds back with “Hi, this is watson. How can I help you?”.

    Now let’s make the bot intelligent by integrating it with IBM Watson’s Personality Insights.

    IBM Watson

    IBM Watson offers AI and Machine Learning services through its Bluemix cloud platform. To get started quickly with Watson and Bluemix, you can create a free account.

    IBM Watson offers many services for AI and Machine Learning. For this example, we will use the Personality Insights services. Use this document to setup and gather credentials for Personality Insights.

    Now update the .env file created above with the Personality Insights credentials.

    GLIP_SERVER=https://platform.devtest.ringcentral.comGLIP_APPKEY=GLIP_APPSECRET=GLIP_USERNAME=GLIP_PASSWORD=GLIP_EXTENSION=WATSON_USERNAME=WATSON_PASSWORD=WATSON_URL=https://gateway.watsonplatform.net/personality-insights/apiWATSON_VERSION_DATE=2017-02-02WATSON_VERSION=v3

    Initiate Personality Insights API using the snippet below:

    var watson = require('watson-developer-cloud'); var personality_insight = watson.personality_insights({username: process.env.WATSON_USERNAME,password: process.env.WATSON_PASSWORD,url: process.env.WATSON_URL,version: process.env.WATSON_VERSION});

    Now let’s integrate it to the chat-bot logic:

    controller.hears(['analyze'], 'message_received', function (bot,message) {bot.api.posts().get({groupId: message.channel}).then(function(history) {

    var messages = [];for(var i=0; i < history.records.length; i++){messages.push(history.records[i].text);}

    var corpus = messages.join("\n");

    personality_insight.profile({text: corpus,language: 'en'},function (err, response) {if(err){console.log('error:', err)}else{controller.startTask(bot, message, function (task, convo) {var top5 = response.personality;for (var c = 0; c < top5.length; c++) {

    convo.say('This channel has ' + Math.round(top5[c].percentile*100) + '% ' + top5[c].name);

    }

    })}})});});

    As you can see, when someone says “analyze”, it will gather the history of the post messages in the group and send it to Watson to analyze. Watson analyzes the post messages and responds back with the top 5 personality insights based on the post messages history as shown below.

    Personality Insight response from IBM Watson Conclusion

    The goal of the post has been to get developers started with building bots in Glip with IBM Watson. Personality Insights is just one of the several API’s available in IBM Watson cloud. You could use other APIs like Tone Analyzer, Text to Speech, Conversation … etc. to build more advanced bots that are fun and interesting. The imagination is the limit!

    A step-by-step tutorial is also available here!

    Give it a try, and I more than welcome any kind of feedback!


    What you missed in Cloud: Watson competitor and an Amazon oops! | killexams.com real questions and Pass4sure dumps

    Olcan SercinogluThe holidays didn’t interrupt the usual flow of activity in the public cloud, where several major developments over the past week set the stage for exciting prospects in the new year.

    A stealth startup called Scaled Inference Inc. fired the opening shot after closing a $13.6 million funding round to build an artificial intelligence service that takes direct aim at IBM’s Watson. The brainchild of former Google engineers Olcan Sercinoglu (right) and Dmitry Lepikhin, Scaled Inference has spent the last year or so trying to remove human involvement from the analytics equation, an approach that contrasts sharply with the efforts of Big Blue and others to position the technology as complementary to existing roles. Making that ambitious goal possible is what the team describes as a unique mathematical model capable of matching problems with answers across a wide range of domains ranging from fraud detection to facial recognition.

    Although the hosted platform is still in early development, Scaled Inference has already raised $27.2 million from investors including the latest round, which bumps its total valuation to a healthy $60 million. That’s especially impressive given that the startup only hit the scene in June, making it one of the newest players to try to capitalize on the exploding demand for cloud services.

    The list of contenders includes other up-and-comers as well established names from adjacent segments such as Huawei Technologies Co. Ltd, which saw an estimated 15 percent revenue increase last year on the back of the trend, according to a New Year’s blog from CEO Ken Hu. The Chinese telecommunications giant invested aggressively in cloud computing over the last year, doubling down on enterprise solutions while expanding its global data center network.

    Huawei is not the only industry heavyweight to have expanded its cloud arsenal lately. Amazon.com Inc. also made headlines last week after accidentally leaking the forthcoming launch of the new C4 instances it unveiled in November. The family is based on a customized version of the latest E5-2666 v3 processor from Intel that runs at a base speed of 2.9 GHz and can peak at 3.5 GHz thanks to built-in software optimizations.

    The now-deleted announcement revealed that C4 machines will range from two virtual proccesors with 3.75 gigabytes of memory and 500Mbps of throughput at $0.116 an hour to 36 virtual processors, 10 gigabyes of memory and 4,000Mbps for $1848 per hour. The series replaces the C3 as the most powerful compute option in Amazon’s portfolio.

    Since you’re here … … We’d like to tell you about our mission and how you can help us fulfill it. SiliconANGLE Media Inc.’s business model is based on the intrinsic value of the content, not advertising. Unlike many online publications, we don’t have a paywall or run banner advertising, because we want to keep our journalism open, without influence or the need to chase traffic.

    The journalism, reporting and commentary on SiliconANGLE — along with live, unscripted video from our Silicon Valley studio and globe-trotting video teams at theCUBE — take a lot of hard work, time and money. Keeping the quality high requires the support of sponsors who are aligned with our vision of ad-free journalism content.

    If you like the reporting, video interviews and other ad-free content here, please take a moment to check out a sample of the video content supported by our sponsors, tweet your support, and keep coming back to SiliconANGLE.


    NVIDIA Delivers Massive Performance Leap for Deep Learning, HPC Applications With NVIDIA Tesla P100 Accelerators | killexams.com real questions and Pass4sure dumps

    Five Architectural Breakthroughs Enable Servers to Deliver Over 12x Greater Performance Than Previous Architecture

    SAN JOSE, CA--(Marketwired - Apr 5, 2016) - GPU Technology Conference -- NVIDIA (NASDAQ: NVDA) today introduced the NVIDIA® Tesla® P100 GPU, the most advanced hyperscale data center accelerator ever built.

    The latest addition to the NVIDIA Tesla Accelerated Computing Platform, the Tesla P100 enables a new class of servers that can deliver the performance of hundreds of CPU server nodes. Today's data centers -- vast network infrastructures with numerous interconnected commodity CPU servers -- process large numbers of transactional workloads, such as web services. But they are inefficient at next-generation artificial intelligence and scientific applications, which require ultra-efficient, lightning-fast server nodes.

    Based on the new NVIDIA Pascal™ GPU architecture with five breakthrough technologies, the Tesla P100 delivers unmatched performance and efficiency to power the most computationally demanding applications.

    "Our greatest scientific and technical challenges -- finding cures for cancer, understanding climate change, building intelligent machines -- require a near-infinite amount of computing performance," said Jen-Hsun Huang, CEO and co-founder, NVIDIA. "We designed the Pascal GPU architecture from the ground up with innovation at every level. It represents a massive leap forward in computing performance and efficiency, and will help some of the smartest minds drive tomorrow's advances."

    Dr. John Kelly III, senior vice president, Cognitive Solutions and IBM Research, said: "As we enter this new era of computing, entirely new approaches to the underlying technologies will be required to fully realize the benefits of AI and cognitive. The combination of NVIDIA GPUs and OpenPOWER technology is already accelerating Watson's learning of new skills. Together, IBM's Power architecture and NVIDIA's Pascal architecture with NVLink will further accelerate cognitive workload performance and advance the artificial intelligence industry."

    Five Architectural Breakthroughs The Tesla P100 delivers its unprecedented performance, scalability and programming efficiency based on five breakthroughs:

  • NVIDIA Pascal architecture for exponential performance leap -- A Pascal-based Tesla P100 solution delivers over a 12x increase in neural network training performance compared with a previous-generation NVIDIA Maxwell™-based solution.
  • NVIDIA NVLink for maximum application scalability -- The NVIDIA NVLink™ high-speed GPU interconnect scales applications across multiple GPUs, delivering a 5x acceleration in bandwidth compared to today's best-in-class solution1. Up to eight Tesla P100 GPUs can be interconnected with NVLink to maximize application performance in a single node, and IBM has implemented NVLink on its POWER8 CPUs for fast CPU-to-GPU communication.
  • 16nm FinFET for unprecedented energy efficiency -- With 15.3 billion transistors built on 16 nanometer FinFET fabrication technology, the Pascal GPU is the world's largest FinFET chip ever built2. It is engineered to deliver the fastest performance and best energy efficiency for workloads with near-infinite computing needs.
  • CoWoS with HBM2 for big data workloads -- The Pascal architecture unifies processor and data into a single package to deliver unprecedented compute efficiency. An innovative approach to memory design, Chip on Wafer on Substrate (CoWoS) with HBM2, provides a 3x boost in memory bandwidth performance, or 720GB/sec, compared to the Maxwell architecture.
  • New AI algorithms for peak performance -- New half-precision instructions deliver more than 21 teraflops of peak performance for deep learning.
  • The Tesla P100 GPU accelerator delivers a new level of performance for a range of HPC and deep learning applications, including the AMBER molecular dynamics code, which runs faster on a single server node with Tesla P100 GPUs than on 48 dual-socket CPU server nodes3. Training the popular AlexNet deep neural network would take 250 dual-socket CPU server nodes to match the performance of eight Tesla P100 GPUs4. And the widely used weather forecasting application, COSMO, runs faster on eight Tesla P100 GPUs than on 27 dual-socket CPU servers5.

    The first accelerator to deliver more than 5 and 10 teraflops of double-precision and single-precision performance, respectively, the Tesla P100 provides a giant leap in processing capabilities and time-to-discovery for research across a broad spectrum of domains.

    Updates to the NVIDIA SDKNVIDIA also announced a host of updates to the NVIDIA SDK, the world's most powerful development platform for GPU computing.

    These updates include NVIDIA CUDA® 8. The latest version of NVIDIA's parallel computing platform gives developers direct access to powerful new Pascal features, including unified memory and NVLink. Also included in this release is a new graph analytics library -- nvGRAPH -- which can be used for robotic path planning, cyber security and logistics analysis, expanding the application of GPU acceleration into the realm of big data analytics.

    NVIDIA also announced cuDNN version 5, a GPU-accelerated library of primitives for deep neural networks. cuDNN 5 includes Pascal GPU support; acceleration of recurrent neural networks, which are used for video and other sequential data; and additional enhancements used in medical, oil and gas, and other industries. cuDNN accelerates leading deep learning frameworks, including Google's TensorFlow, UC Berkeley's Caffe, University of Montreal's Theano and NYU's Torch. These, in turn, power deep learning solutions used by Amazon, Facebook, Google and others.

    Tesla P100 Specifications Specifications of the Tesla P100 GPU accelerator include:

  • 5.3 teraflops double-precision performance, 10.6 teraflops single-precision performance and 21.2 teraflops half-precision performance with NVIDIA GPU BOOST™ technology
  • 160GB/sec bi-directional interconnect bandwidth with NVIDIA NVLink
  • 16GB of CoWoS HBM2 stacked memory
  • 720GB/sec memory bandwidth with CoWoS HBM2 stacked memory
  • Enhanced programmability with page migration engine and unified memory
  • ECC protection for increased reliability
  • Server-optimized for highest data center throughput and reliability
  • Availability General availability for the Pascal-based NVIDIA Tesla P100 GPU accelerator in the new NVIDIA DGX-1™ deep learning system is in June. It is also expected to be available beginning in early 2017 from leading server manufacturers.

    Supporting Resources

    Keep Current on NVIDIA Subscribe to the NVIDIA blog, follow us on Facebook, Google+, Twitter, LinkedIn and Instagram, and view NVIDIA videos on YouTube and images on Flickr.

    About NVIDIA NVIDIA (NASDAQ: NVDA) is a computer technology company that has pioneered GPU-accelerated computing. It targets the world's the most demanding users -- gamers, designers and scientists -- with products, services and software that power amazing experiences in virtual reality, artificial intelligence, professional visualization and autonomous cars. More information at http://nvidianews.nvidia.com/.

    (1) NVLink delivers 160GB/sec of bi-directional interconnect bandwidth, compared to PCIe x16 Gen3 that delivers 31.5GB/sec of bi-directional bandwidth. (2) NVIDIA Tesla P100 GPU has 15.3 billion 16nm FinFET transistors.(3) CPU system: 48 nodes, each node with 2x Intel E5-2680v3 12 core, 128GB DDR4, FDR IB interconnect. GPU system: Single node, 2x Intel E5-2698 v3 16 core, 512GB DDR4, 4x Tesla P100, NVLink interconnect.(4) Compared to Caffe/AlexNet time to train ILSVRC-2012 dataset on cluster of two-socket Intel Xeon E5-2697 v3 processor-based systems with InfiniBand interconnect. 250-node performance estimated using source: https://software.intel.com/en-us/articles/caffe-training-on-multi-node-distributed-memory-systems-based-on-intel-xeon-processor-e5.(5) CPU system: 2x Intel E5-2698 v3 16 core, 256GB DDR4. GPU system: Single node, 2x Intel E5-2698 v3 16 core, 512GB DDR4, 8x Tesla P100, NVLink interconnect.

    Certain statements in this press release including, but not limited to, statements as to: the impact, performance, benefits and availability of the NVIDIA Tesla P100 GPU, the NVIDIA SDK and NVIDIA DGX-1 deep learning system are forward-looking statements that are subject to risks and uncertainties that could cause results to be materially different than expectations. Important factors that could cause actual results to differ materially include: global economic conditions; our reliance on third parties to manufacture, assemble, package and test our products; the impact of technological development and competition; development of new products and technologies or enhancements to our existing product and technologies; market acceptance of our products or our partners' products; design, manufacturing or software defects; changes in consumer preferences or demands; changes in industry standards and interfaces; unexpected loss of performance of our products or technologies when integrated into systems; as well as other factors detailed from time to time in the reports NVIDIA files with the Securities and Exchange Commission, or SEC, including its Form 10-K for the fiscal year ended January 31, 2016. Copies of reports filed with the SEC are posted on the company's website and are available from NVIDIA without charge. These forward-looking statements are not guarantees of future performance and speak only as of the date hereof, and, except as required by law, NVIDIA disclaims any obligation to update these forward-looking statements to reflect future events or circumstances.

    © 2016 NVIDIA Corporation. All rights reserved. NVIDIA, the NVIDIA logo, Tesla, Pascal, Maxwell, NVIDIA NVLink, CUDA, NVIDIA GPU BOOST, and DGX-1 are trademarks and/or registered trademarks of NVIDIA Corporation in the U.S. and other countries. Other company and product names may be trademarks of the respective companies with which they are associated. Features, pricing, availability and specifications are subject to change without notice.



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