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00M-502 Power Systems with IBM i Sales Professional - v2

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00M-502 exam Dumps Source : Power Systems with IBM i Sales Professional - v2

Test Code : 00M-502
Test appellation : Power Systems with IBM i Sales Professional - v2
Vendor appellation : IBM
: 45 actual Questions

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IBM Power Systems with IBM

international Cloud data middle market will multiply at striking CAGR of 28.7% by route of 2023 || suitable Key avid gamers are Microsoft, AWS, Cisco techniques, IBM and Google | killexams.com actual Questions and Pass4sure dumps

Cloud information core Market – international Drivers, Restraints, opportunities, developments, and Forecasts to 2023

Market Overview:Cloud computing is a metamorphosis within the IT trade changing the character of the competitors inside the laptop industry. organizations, akin to Cisco systems, Amazon, Google, Microsoft, and IBM, correspond with that the cloud could subsist controlling the IT materials as well as assisting in reducing TCO. Cloud computing revolutionizes the route IT supplies are being deployed, configured, and managed. With the boost within the expend of cloud computing, storage atmosphere has moreover modified. There are a brace of converging traits comparable to a climb in number of users, IT consumerization, and more data and contraptions pushing the storage ambiance to a brand new stage. These applied sciences don't seem to subsist only driving the cloud market, however moreover pushing the expend of applied sciences a grand route to assist cloud computing.

Cloud information middle is a repository, which can subsist both physical or digital used for administration, storage, and transmission of information. It requires minimal renovation can suffuse when compared to the servers. they contain their own vigour supply in case of electricity failure. they are cost-efficient and require much less resource, thereby attracting end-clients to originate expend of cloud deployment rather than servers.

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Market analysis:in keeping with Infoholic research, the Cloud statistics middle market is expected to attain $67.5 billion through 2023, becoming at a CAGR of round 28.7% perfect over the forecast period. groups are reassessing their records centres and locating to divide areas. They are attempting to originate expend of rising applied sciences to build dynamic, scalable next-generation facts middle with the goal to response to altering enterprise needs with out expanding the can suffuse and overspending on provider degrees. moreover, there is transforming into claim from North American groups because the cloud expertise will assist in storing statistics and suggestions charge-simply, expanding the effectivity, agility, and flexibility of the statistics middle.software evaluation:software is the fundamental boosting ingredient for the cloud records centers market. The foremost expertise suppliers contain joined hands with the cloud and third-birthday party suppliers to present imaginitive facts seat options, which allow the conclusion-users to shop their facts on the cloud with stronger security elements. The software market is extra segmented into storage, server, and networking. In 2017, cloud storage had an immense participate of the market and is expected to achieve $22.ninety nine billion through 2023, growing to subsist at a CAGR of 27.6% perquisite through the forecast age 2017–2023.Regional analysis:currently, the Americas is having nearly perfect of the market participate mainly as a result of developments in technology. The market is witnessing in claim boom in nations such as the US and Canada. Americas is followed via Europe when it comes to exorbitant market share. The regional government initiatives for the expend of cloud computing and reducing the operational can suffuse fosters the growth in the region. APAC is spending vastly in construction of facts centers and sharing expertise concerning the expend of cloud computing. LAMEA is expected to contain a conducive growth out there, as a route to offer large alternatives for key players to supply creative options in the region.

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Key gamers:the key avid gamers covered in the file are Microsoft, AWS, Cisco programs, IBM, and Google.aggressive evaluation:The resolve covers and analyzes the Cloud data middle market. Bringing out the comprehensive key insights of the trade, the record aims to supply a casual for gamers to recall the latest traits, present market state of affairs, government initiatives, and technologies related to the market. additionally, it helps the challenge capitalists in figuring out the companies more desirable and recall suggested decisions.benefits:The file offers an in-depth evaluation of the cloud information seat market aiming to sever back time to market for products and functions, reduce operational and cooling costs, improve company efficiency, and operational efficiency. The document talks about software, capabilities, service fashions, end-consumer, and areas. With the assistance provided, key stakeholders can recognize in regards to the most vital tendencies, drivers, investments, plumb participant’s initiatives, and public private partnership (PPP’s) interest toward the adoption of the know-how. extra, the report provides details concerning the vital challenges impacting market growth.Key Stakeholders:data core producers, community providers, software providers, records safety respond carriers, policy makers, ordinary construction agencies, investor neighborhood, college researchers, blog writers, and know-how magazines.

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IBM power system S922: Rack Server Overview and perception | killexams.com actual Questions and Pass4sure dumps

See the complete checklist of accurate rack server carriers

bottom line:

The IBM power programs S922 server is designed from the floor up for statistics intensive workloads relish databases or analytics. it may possibly sheperd a brace of key enterprise information-intensive situations, together with mainstream applications, main-side HPC workloads and evolving synthetic intelligence (AI) projects.

valued clientele seeking major compute vigour may still know this key reality: POWER9 solutions are the basis of the world’s first and third quickest supercomputers, the U.S. department of energy’s zenith and Sierra installations.

IBM vitality servers are inclined to contain a much better cost of entry than x86 machines. although, in line with a glimpse at with the aid of Quark + Lepton, IBM energy programs running IBM I application contain 60% lower total cost of ownership than windows/SQL Server or X86 primarily based Oracle methods. IBM’s pitch is that there are limits to what commodity architectures can do.

youngsters, in case you forecast surges renowned and don’t contain margin for downtime, licensing charges, or occasional crashes a better traffic structure could subsist required.

Product description:

The S922 is a 1 or 2 socket server that presents a wide variety of core configurations and up to 4 TB of memory. Chip core speeds on the four-core are 2.8 to 3.8GHz, on the 8 core are 3.four to 3.9 GHz and on the ten core are 2.9 to 3.eight GHz. the one socket edition gives up to 6 PCIe ( 2 x Gen4 and four x Gen3) slots and both socket version gives as much as 9 slots (three more Gen4 slots). One slot is used via a mandatory Ethernet adapter. counting on what is attached, up to a few of those slots may well subsist reserved for different applications. IBM i is barely supported on the 6 cores and 8 core processors and is limited to four cores of IBM i with a software tier of P10.

vigour methods are regularly occurring for his or her RAS (resiliency, availability, serviceability) points. IBM POWER9-primarily based programs are stated to deliver up to 10X sooner bandwidth acceleration and 50% more suitable recollection bandwidth than similar x86 options. They moreover aid the newest in information switch applied sciences, together with PCIe 4.0 and novel NVLink and OpenCAPI interfaces. This new server era comes along with twice the recollection footprint than POWER8. adjustments within the recollection subsystem and using the newest DIMMs boost expense/performance.

features:

number of processors:

up to 2

Processors supported:

IBM POWER9 Scale-Out SMT8 processor (12-core, 10-core, eight-core, 4-core offerings)

Cores per processor:

4, eight,10 cores per socket

highest processor frequency/cache:

3.9 GHz/512k L2 and 10 MB L3

I/O expansion slots:

the separate socket version offers up to six PCIe ( 2 x Gen4 and four x Gen3) slots and the two socket edition provides up to 9 slots (three extra Gen4 slots). One slot is used via a compulsory Ethernet adapter. reckoning on what's attached, up to a few of those slots could subsist reserved for other applications.

One entrance USB three.0 ports – Two rear USB three.0 ports – Two HMC 1 GbE RJ45 ports – One gear port with RJ45 connector – 1x USB three.0 entrance, 2x USB 3.0 rear, 2x HMC 1 GB Eth RJ45 ports, one device port with RJ45 connector, 2x tall velocity 25 Gb/s ports

highest memory/# slots/velocity:

up to 4 TB/32 IS RDIMM slots/as much as 2666 Mhz

optimum Persistent memory:

NA 

Storage controller:

S922/S924 has two internal direct attached storage connectors, an NVMe card and a SAS card

support:

The electronic features web portal is a separate web entry point that replaces the diverse entry elements traditionally used to entry IBM internet capabilities and guide. This internet portal makes it feasible for you to benefit less difficult entry to IBM resources for counsel in resolving technical problems. The newly superior My systems and top class Search services originate it even easier for electronic carrier Agent-enabled consumers to music gadget stock and determine pertinent fixes.

My systems gives helpful studies of installed hardware and application the usage of guidance gathered from the methods by using IBM digital carrier Agent. stories are available for any system linked to the client's IBMid. premium Search combines the role of search and the cost of electronic provider Agent counsel, offering advanced search of the technical aid knowledgebase.

“it's a transparent selection if you contain already got an established IBM AIX ambiance and want to preserve compatibility and sustain performance. There are comparable alternate options now which may well subsist capable of rep you to 3 nines for 1/2 the charges,” pointed out a Senior supervisor of IT in the manufacturing industry. 

Key markets and expend cases:

IBM power systems S922 server readily integrates into a company’s cloud & cognitive strategy and gives you advanced expense performance for mission crucial workloads.

POWER9 is designed from the floor up for records intensive workloads relish databases or analytics

cost:

20 core, 512 GB, $37,222. The utility is tall priced.

“it is a product with exorbitant efficiency, efficiency and fiscal indices in the IT market,” talked about an functions Engineering within the education business. "Deployment is terribly effortless, however took more than three months. It proved cost efficient in the long run.”

Server

IBM vitality S922

Max Processor Frequency

three.9 GHz/512k L2 and 10 MB L3

Max Persistent reminiscence

N/A

kind element

2U

Max Processors

2 POWER9 Scale-Out SMT8

Max memory

4 TB

Max Storage

four TB

rate

$37,222

Key Differentiator

precise processing vigour


energy Couple: IBM Joins PowerAI Hardware with Watson; Launches Multi-Hybrid AI functions | killexams.com actual Questions and Pass4sure dumps

As IBM kicked off its annual Thinkconference, a high-production extravaganza this week in San Francisco, the company this morning made a sequence of AI-connected announcements, together with the merging of its PowerAI built-in CPU-GPU server line with its cognitive computing flagship IBM Watson, to create what it calls the Watson desktop getting to know Accelerator (WML Accelerator), which huge Blue claims hurries up computer gaining scholarship of working towards 46X.

The purpose of the Watson-energy marriage, IBM spoke of, is to create a converged, integrated AI respond “that brings collectively the most desirable software for AI, Watson, with the most appropriate hardware for AI, IBM vitality methods” – the superior goal being to ease traffic AI adoption. As section of that strategy, the enterprise this week will expose new benchmarks for SnapML, IBM’s ML framework for simplified model alternative and hyper-parameter tuning, which always requires the really grand scholarship of facts scientists.

“by means of scaling out throughout a cluster, in addition to scaling up across many-core CPUs and stout contemporary GPUs, SnapML is capable of deciding upon a highly correct model and its hyper-parameter configuration extremely immediately,” talked about Sumit Gupta, VP of AI and HPC, IBM Cognitive programs, in a weblog.

Like many different AI-linked vendor announcements during the ultimate year, IBM’s vigour AI-Watson convergence is aimed at easing the AI linked skills shortage, illustrious via 54 p.c as a barrier to AI adoption in a recent Gartner CIO survey, together with a potential scarcity concerning integrating AI into existing infrastructures, referred to by route of 27 % of those in the Gartner surveyed with the aid of Gartner. WML Accelerator is designed to back agencies train and install ML models in-built IBM Watson Studio and monitored with Watson OpenScale.

IBM’s Sumit Gupta

At IBM suppose ultimate 12 months, the traffic demonstrated the SnapML library working on vigour techniques servers and reported that it beat Google Cloud running ML on an promoting-focused dataset via 46x. in view that then, the company has built-in new automation facets that scale out across a cluster and scale up across many-core CPUs and GPUs, making “SnapML… in a position to selecting a extremely accurate model and its hyper-parameter configuration extremely without delay.”

“Many clients don’t understand how immense the open supply desktop learning catalogue is, and it will moreover subsist rather difficult to identify the appropriate device for your selected statistics or favored outcome,” referred to Simon Thompson, analysis Computing Infrastructure Architect on the university of Birmingham. “The automatic mannequin and library option capabilities of SnapML tremendously reduce the time required to parse through perfect of these tools, permitting users to start ML training a considerable deal more directly.”

You may moreover examine the relaxation of this memoir at EnterpriseTech.


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Whirlpool signs IBM as cloud management partner | killexams.com actual questions and Pass4sure dumps

Whirlpool Corporation has turned to IBM to attend it manage its bevy of global digital operations in the cloud.

The Benton Harbor-based appliance maker said ultimate month it will expend IBM Cloud “to manage and provide 24/7 access to the company’s faultfinding enterprise applications,” including enterprise resource planning, customer relationship management and traffic warehousing.

Whirlpool plans to leverage the “flexibility and scalability” of the IBM Cloud to race Whirlpool’s SAP HANA environments, including around-the-clock insights into its 70 global manufacturing sites, its distribution and convene centers and several other systems to provide “worldwide access and increased efficiencies.”

SAP HANA is a database management system developed by Walldorf, Germany-based SAP.

“Whirlpool Corporation is a manufacturing leader, and they contain moved their work to the cloud aggressively,” said Mike Berendsen, VP of IT, Whirlpool Corporation. “This collaboration with IBM will continue to allow us to leverage the capabilities of SAP on HANA, as they continue to subsist a tech-forward company in the products they design and their global work environments.”

IBM recently introduced a new SAP-certified managed applications offering for SAP HANA workloads. It is an integrated approach for hardware, software and services, and the company said it is the first to offer “this flat of cloud support” for workloads of up to 24 terabytes by using IBM Power Systems and IBM Storage. IBM said the approach will give “even the largest customers” the capacity they requisite to manage analytics in the cloud.

IBM said its innovation was “key” to gaining Whirlpool as a client, as the company has traffic requirements that drive “very large” HANA platforms exceeding “generally available” cloud capacities for HANA.

“The advent of emerging technologies has provided companies relish Whirlpool Corporation with a entire new platform to innovate on,” said designate Slaga, global technology services universal manager, IBM Services for Managed Applications. “Prior solutions often lacked the talent to scale to meet the needs of clients relish Whirlpool and required substantial customization, which could drive up costs.

“With this solution, we’re providing Whirlpool Corporation the means to scale its HANA environment to sustain up on strategy.”

Under the new partnership, Whirlpool now has options to expand its SAP HANA portfolio and “can focus on what’s most vital to its business,” IBM said.

Whirlpool

Whirlpool Corporation (NYSE: WHR) is an appliance manufacturer with 92,000 employees and 70 manufacturing and technology research centers around the world.

The company markets the brands Whirlpool, KitchenAid, Maytag, Consul, Brastemp, Amana, Bauknecht, Jenn-Air, Indesit and other major brand names.

The company reported 2017 sales of $21 billion.

IBM Services

Founded in 1995, IBM Services is the professional services arm of Armonk, New York-based IBM.

The division uses advanced technology to attend clients reduce cost and risk, achieve compliance, accelerate speed to market, create new revenue streams and establish a “security-rich and reliable” infrastructure that’s ready for AI and hybrid cloud.

It offers various services: applications services; traffic process, operations, resiliency, strategy and design; cloud, digital workplace, network and security services; and technology back and consulting.


How IBM Watson Overpromised and Underdelivered on AI Health suffuse | killexams.com actual questions and Pass4sure dumps

Editor’s Picks

Making Medical AI Trustworthy and Transparent

Layoffs at Watson Health betray IBM’s Problem With AI

AI vs. Doctors

Conceptual photo-illustration imagining IBM’s AI Watson as a concerned doctor, with the Watson logo standing in for the doctor’s face. Illustration: Eddie Guy

In 2014, IBM opened swanky new headquarters for its simulated intelligence division, known as IBM Watson. Inside the glassy tower in lower Manhattan, IBMers can bring prospective clients and visiting journalists into the “immersion room,” which resembles a miniature planetarium. There, in the darkened space, visitors sit on swiveling stools while fancy graphics sparkle around the curved screens covering the walls. It’s the closest you can get, IBMers sometimes say, to being inside Watson’s electronic brain.

One dazzling 2014 demonstration of Watson’s brainpower showed off its potential to transform medicine using AI—a goal that IBM CEO Virginia Rometty often calls the company’s moon shot. In the demo, Watson took a bizarre collection of patient symptoms and came up with a list of feasible diagnoses, each annotated with Watson’s confidence flat and links to supporting medical literature.

Within the snug confines of the dome, Watson never failed to impress: Its recollection banks held scholarship of every rare disease, and its processors weren’t susceptible to the kind of cognitive jaundice that can hurl off doctors. It could crack a tough case in mere seconds. If Watson could bring that instant expertise to hospitals and clinics perfect around the world, it seemed feasible that the AI could reduce diagnosis errors, optimize treatments, and even alleviate doctor shortages—not by replacing doctors but by helping them attain their jobs faster and better.

Project: Oncology Expert Advisor

MD Anderson Cancer seat partnered with IBM Watson to create an advisory tool for oncologists. The tool used natural-language processing (NLP) to summarize patients’ electronic health records, then searched databases to provide treatment recommendations. Physicians tried out a prototype in the leukemia department, but MD Anderson canceled the project in 2016—after spending US $62 million on it.

Outside of corporate headquarters, however, IBM has discovered that its powerful technology is no match for the messy reality of today’s health suffuse system. And in trying to apply Watson to cancer treatment, one of medicine’s biggest challenges, IBM encountered a fundamental mismatch between the route machines learn and the route doctors work.

IBM’s bold attempt to revolutionize health suffuse began in 2011. The day after Watson thoroughly defeated two human champions in the game of Jeopardy!, IBM announced a new career path for its AI quiz-show winner: It would become an AI doctor. IBM would recall the breakthrough technology it showed off on television—mainly, the talent to understand natural language—and apply it to medicine. Watson’s first commercial offerings for health suffuse would subsist available in 18 to 24 months, the company promised.

In fact, the projects that IBM announced that first day did not submit commercial products. In the eight years since, IBM has trumpeted many more high-profile efforts to develop AI-powered medical technology—many of which contain fizzled, and a few of which contain failed spectacularly. The company spent billions on acquisitions to bolster its internal efforts, but insiders voice the acquired companies haven’t yet contributed much. And the products that contain emerged from IBM’s Watson Health division are nothing relish the brilliant AI doctor that was once envisioned: They’re more relish AI assistants that can accomplish certain routine tasks.

“Reputationally, I arbitrator they’re in some trouble,” says Robert Wachter, chair of the department of medicine at the University of California, San Francisco, and author of the 2015 book The Digital Doctor: Hope, Hype, and Harm at the Dawn of Medicine’s Computer Age (McGraw-Hill). In part, he says, IBM is suffering from its ambition: It was the first company to originate a major propel to bring AI to the clinic. But it moreover earned ill will and skepticism by boasting of Watson’s abilities. “They came in with marketing first, product second, and got everybody excited,” he says. “Then the rubber hit the road. This is an incredibly hard set of problems, and IBM, by being first out, has demonstrated that for everyone else.”

Since 2011, IBM Watson has announced a multitude of projects in health care. How contain they fared? graphic link to table at  conclude of article contemplate table “So Far, Few Successes”

At a 2017 conference of health IT professionals, IBM CEO Rometty told the crowd that AI “is real, it’s mainstream, it’s here, and it can change almost everything about health care,” and added that it could usher in a medical “golden age.” She’s not alone in seeing an opportunity: Experts in computer science and medicine alike correspond that AI has the potential to transform the health suffuse industry. Yet so far, that potential has primarily been demonstrated in carefully controlled experiments. Only a few AI-based tools contain been approved by regulators for expend in actual hospitals and doctors’ offices. Those pioneering products work mostly in the visual realm, using computer vision to resolve images relish X-rays and retina scans. (IBM does not contain a product that analyzes medical images, though it has an active research project in that area.)

Looking beyond images, however, even today’s best AI struggles to originate sense of intricate medical information. And encoding a human doctor’s expertise in software turns out to subsist a very tricky proposition. IBM has learned these painful lessons in the marketplace, as the world watched. While the company isn’t giving up on its moon shot, its launch failures contain shown technologists and physicians alike just how difficult it is to build an AI doctor.

The Jeopardy! victory in 2011 showed Watson’s remarkable skill with natural-language processing (NLP). To play the game, it had to parse complicated clues replete of wordplay, search massive textual databases to find feasible answers, and determine the best one. Watson wasn’t a glorified search engine; it didn’t just recur documents based on keywords. Instead it employed hundreds of algorithms to map the “entities” in a sentence and understand the relationships among them. It used this skill to originate sense of both the Jeopardy! clue and the millions of text sources it mined.

Project: Cognitive Coaching System

The sportswear company Under Armour teamed up with Watson Health to create a “personal health trainer and tness consultant.” Using data from Under Armour’s activity-tracker app, the Cognitive Coach was intended to provide customized training programs based on a user’s habits, as well as advice based on analysis of outcomes achieved by similar people. The coach never launched, and Under Armour is no longer working with IBM Watson.

“It almost seemed that Watson could understand the import of language, rather than just recognizing patterns of words,” says Martin Kohn, who was the chief medical scientist for IBM Research at the time of the Jeopardy! match. “It was an order of magnitude more powerful than what existed.” What’s more, Watson developed this talent on its own, via machine learning. The IBM researchers trained Watson by giving it thousands of Jeopardy! clues and responses that were labeled as correct or incorrect. In this intricate data set, the AI discovered patterns and made a model for how to rep from an input (a clue) to an output (a correct response).

Long before Watson starred on the Jeopardy! stage, IBM had considered its possibilities for health care. Medicine, with its reams of patient data, seemed an obvious fit, particularly as hospitals and doctors were switching over to electronic health records. While some of that data can subsist easily digested by machines, such as lab results and vital-sign measurements, the bulk of it is “unstructured” information, such as doctor’s notes and hospital discharge summaries. That narrative text accounts for about 80 percent of a typical patient’s record—and it’s a stew of jargon, shorthand, and subjective statements.

Kohn, who came to IBM with a medical degree from Harvard University and an engineering degree from MIT, was excited to attend Watson tackle the language of medicine. “It seemed relish Watson had the potential to overcome those complexities,” he says. By turning its mighty NLP abilities to medicine, the theory went, Watson could read patients’ health records as well as the entire corpus of medical literature: textbooks, peer-reviewed journal articles, lists of approved drugs, and so on. With access to perfect this data, Watson might become a superdoctor, discerning patterns that no human could ever spot.

“Doctors fade to work every day—especially the people on the front lines, the primary suffuse doctors—with the understanding that they cannot possibly know everything they requisite to know in order to practice the best, most efficient, most efficient medicine possible,” says Herbert Chase, a professor of medicine and biomedical informatics at Columbia University who collaborated with IBM in its first health suffuse efforts. But Watson, he says, could sustain up—and if turned into a tool for “clinical decision support,” it could enable doctors to sustain up, too. In lieu of a Jeopardy! clue, a physician could give Watson a patient’s case history and hope for a diagnosis or optimal treatment plan.

Chase worked with IBM researchers on the prototype for a diagnostic tool, the thing that dazzled visitors in the Watson immersion room. But IBM chose not to commercialize it, and Chase parted ways with IBM in 2014. He’s disappointed with Watson’s leisurely progress in medicine since then. “I’m not aware of any spectacular home runs,” he says.

He’s one of many early Watson enthusiasts who are now dismayed. Eliot Siegel, a professor of radiology and vice chair of information systems at the University of Maryland, moreover collaborated with IBM on the diagnostic research. While he thinks AI-enabled tools will subsist essential to doctors within a decade, he’s not confident that IBM will build them. “I don’t arbitrator they’re on the cutting edge of AI,” says Siegel. “The most exciting things are going on at Google, Apple, and Amazon.”

As for Kohn, who left IBM in 2014, he says the company fell into a common trap: “Merely proving that you contain powerful technology is not sufficient,” he says. “Prove to me that it will actually attain something useful—that it will originate my life better, and my patients’ lives better.” Kohn says he’s been waiting to contemplate peer-reviewed papers in the medical journals demonstrating that AI can improve patient outcomes and rescue health systems money. “To date there’s very runt in the route of such publications,” he says, “and not any of consequence for Watson.”

AI’s First Foray Into Health Care Doctors are a conservative bunch—for grand reason—and leisurely to adopt new technologies. But in some areas of health care, medical professionals are birth to contemplate artificially brilliant systems as reliable and helpful. Here are a few early steps toward AI medicine. Robotic Surgery Image Analysis Genetic Analysis Pathology Currently used only for routine steps in simple procedures relish laser eye surgery and hair transplants. Experts are just birth to expend automated systems to attend them examine X-rays, retina scans, and other images. With genome scans becoming a routine section of medicine, AI tools that quickly draw insights from the data are becoming necessary. Experimental systems contain proved adept at analyzing biopsy samples, but aren’t yet approved for clinical use. Clinical-Decision support  Virtual Nursing Medical Administration Mental Health Hospitals are introducing tools for applications relish predicting septic shock, but they haven’t yet proved their value. Rudimentary systems can check on patients between office visits and provide automatic alerts to physicians. Companies are rushing to offer AI-enabled tools that can expand efficiency in tasks relish billing and insurance claims. Researchers are exploring such applications as monitoring depression by mining mobile phone and social media data.

In trying to bring AI into the clinic, IBM was taking on an mammoth technical challenge. But having fallen behind tech giants relish Google and Apple in many other computing realms, IBM needed something broad to sojourn relevant. In 2014, the company invested US $1 billion in its Watson unit, which was developing tech for multiple traffic sectors. In 2015, IBM announced the formation of a special Watson Health division, and by mid-2016 Watson Health had acquired four health-data companies for a total cost of about $4 billion. It seemed that IBM had the technology, the resources, and the commitment necessary to originate AI work in health care.

Today, IBM’s leaders talk about the Watson Health ail as “a journey” down a road with many twists and turns. “It’s a difficult task to inject AI into health care, and it’s a challenge. But we’re doing it,” says John E. Kelly III, IBM senior vice president for cognitive solutions and IBM research. Kelly has guided the Watson ail since the Jeopardy! days, and in late 2018 he moreover assumed direct oversight of Watson Health. He says the company has pivoted when it needs to: “We’re continuing to learn, so their offerings change as they learn.”

Project: Sugar.IQ

Medtronic and Watson Health began working together in 2015 on an app for personalized diabetes management. The app works with data from Medtronic’s continuous glucose monitor, and helps diabetes patients track how their medications, food, and lifestyle choices affect their glucose levels. The FDA-approved app launched in 2018.

The diagnostic tool, for example, wasn’t brought to market because the traffic case wasn’t there, says Ajay Royyuru, IBM’s vice president of health suffuse and life sciences research. “Diagnosis is not the Place to go,” he says. “That’s something the experts attain pretty well. It’s a hard task, and no matter how well you attain it with AI, it’s not going to displace the expert practitioner.” (Not everyone agrees with Royyuru: A 2015 report on diagnostic errors from the National Academies of Sciences, Engineering, and Medicine stated that improving diagnoses represents a “moral, professional, and public health imperative.”)

In an attempt to find the traffic case for medical AI, IBM pursued a dizzying number of projects targeted to perfect the different players in the health suffuse system: physicians, administrative staff, insurers, and patients. What ties perfect the threads together, says Kelly, is an ail to provide “decision back using AI [that analyzes] massive data sets.” IBM’s most publicized project focused on oncology, where it hoped to deploy Watson’s “cognitive” abilities to revolve broad data into personalized cancer treatments for patients.

In many attempted applications, Watson’s NLP struggled to originate sense of medical text—as contain many other AI systems. “We’re doing incredibly better with NLP than they were five years ago, yet we’re still incredibly worse than humans,” says Yoshua Bengio, a professor of computer science at the University of Montreal and a leading AI researcher. In medical text documents, Bengio says, AI systems can’t understand ambiguity and don’t pick up on subtle clues that a human doctor would notice. Bengio says current NLP technology can attend the health suffuse system: “It doesn’t contain to contain replete understanding to attain something incredibly useful,” he says. But no AI built so far can match a human doctor’s comprehension and insight. “No, we’re not there,” he says.

IBM’s work on cancer serves as the prime case of the challenges the company encountered. “I don’t arbitrator anybody had any concept it would recall this long or subsist this complicated,” says designate Kris, a lung cancer specialist at Memorial Sloan Kettering Cancer Center, in New York City, who has led his institution’s collaboration with IBM Watson since 2012.

The ail to improve cancer suffuse had two main tracks. Kris and other preeminent physicians at Sloan Kettering trained an AI system that became the product Watson for Oncology in 2015. Across the country, preeminent physicians at the University of Texas MD Anderson Cancer Center, in Houston, collaborated with IBM to create a different tool called Oncology Expert Advisor. MD Anderson got as far as testing the tool in the leukemia department, but it never became a commercial product.

Both efforts contain received stout criticism. One excoriating article about Watson for Oncology alleged that it provided useless and sometimes unsafe recommendations (IBM contests these allegations). More broadly, Kris says he has often heard the critique that the product isn’t “real AI.” And the MD Anderson project failed dramatically: A 2016 audit by the University of Texas create that the cancer seat spent $62 million on the project before canceling it. A deeper glimpse at these two projects reveals a fundamental mismatch between the engage of machine learning and the reality of medical care—between “real AI” and the requirements of a functional product for today’s doctors.

Watson for Oncology was hypothetical to learn by ingesting the vast medical literature on cancer and the health records of actual cancer patients. The hope was that Watson, with its mighty computing power, would examine hundreds of variables in these records—including demographics, tumor characteristics, treatments, and outcomes—and determine patterns invisible to humans. It would moreover sustain up to date with the bevy of journal articles about cancer treatments being published every day. To Sloan Kettering’s oncologists, it sounded relish a potential breakthrough in cancer care. To IBM, it sounded relish a considerable product. “I don’t arbitrator anybody knew what they were in for,” says Kris.

Watson learned fairly quickly how to scan articles about clinical studies and determine the basic outcomes. But it proved impossible to teach Watson to read the articles the route a doctor would. “The information that physicians extract from an article, that they expend to change their care, may not subsist the major point of the study,” Kris says. Watson’s thinking is based on statistics, so perfect it can attain is amass statistics about main outcomes, explains Kris. “But doctors don’t work that way.”

In 2018, for example, the FDA approved a new “tissue agnostic” cancer drug that is efficient against perfect tumors that exhibit a specific genetic mutation. The drug was fast-tracked based on dramatic results in just 55 patients, of whom four had lung cancer. “We’re now saw that every patient with lung cancer should subsist tested for this gene,” Kris says. “All the prior guidelines contain been thrown out, based on four patients.” But Watson won’t change its conclusions based on just four patients. To resolve this problem, the Sloan Kettering experts created “synthetic cases” that Watson could learn from, essentially make-believe patients with certain demographic profiles and cancer characteristics. “I believe in analytics; I believe it can uncover things,” says Kris. “But when it comes to cancer, it really doesn’t work.”

Do You Agree? Several studies contain compared Watson for Oncology’s cancer treatment recommendations to those of hospital oncologists. The concordance percentages bespeak how often Watson’s advice matched the experts’ treatment plans. pie chart Sources: Journal of Clinical Oncology 2017; Cancer Research 2017; JCO Clinical Cancer Informatics 2018

The realization that Watson couldn’t independently extract insights from breaking news in the medical literature was just the first strike. Researchers moreover create that it couldn’t mine information from patients’ electronic health records as they’d expected.

At MD Anderson, researchers do Watson to work on leukemia patients’ health records—and quickly discovered how tough those records were to work with. Yes, Watson had phenomenal NLP skills. But in these records, data might subsist missing, written down in an cryptic way, or out of chronological order. In a 2018 paper published in The Oncologist, the team reported that its Watson-powered Oncology Expert Advisor had variable success in extracting information from text documents in medical records. It had accuracy scores ranging from 90 to 96 percent when dealing with transparent concepts relish diagnosis, but scores of only 63 to 65 percent for time-dependent information relish therapy timelines.

In a final blow to the dream of an AI superdoctor, researchers realized that Watson can’t compare a new patient with the universe of cancer patients who contain Come before to determine hidden patterns. Both Sloan Kettering and MD Anderson hoped that the AI would mimic the abilities of their expert oncologists, who draw on their experience of patients, treatments, and outcomes when they devise a strategy for a new patient. A machine that could attain the identical nature of population analysis—more rigorously, and using thousands more patients—would subsist hugely powerful.

But the health suffuse system’s current standards don’t cheer such real-world learning. MD Anderson’s Oncology Expert Advisor issued only “evidence based” recommendations linked to official medical guidelines and the outcomes of studies published in the medical literature. If an AI system were to ground its advice on patterns it discovered in medical records—for example, that a certain nature of patient does better on a certain drug—its recommendations wouldn’t subsist considered evidence based, the gold measure in medicine. Without the strict controls of a scientific study, such a finding would subsist considered only correlation, not causation.

Kohn, formerly of IBM, and many others arbitrator the standards of health suffuse must change in order for AI to realize its replete potential and transform medicine. “The gold measure is not really gold,” Kohn says. AI systems could consider many more factors than will ever subsist represented in a clinical trial, and could sort patients into many more categories to provide “truly personalized care,” Kohn says. Infrastructure must change too: Health suffuse institutions must correspond to participate their proprietary and privacy-controlled data so AI systems can learn from millions of patients followed over many years.

According to anecdotal reports, IBM has had peril finding buyers for its Watson oncology product in the United States. Some oncologists voice they reliance their own judgment and don’t requisite Watson telling them what to do. Others voice it suggests only measure treatments that they’re well aware of. But Kris says some physicians are finding it useful as an instant second feeling that they can participate with nervous patients. “As imperfect as it is, and limited as it is, it’s very helpful,” Kris says. IBM sales reps contain had more luck outside the United States, with hospitals in India, South Korea, Thailand, and beyond adopting the technology. Many of these hospitals proudly expend the IBM Watson brand in their marketing, telling patients that they’ll subsist getting AI-powered cancer care.

Conceptual photo-illustration imagining IBM’s AI Watson as a concerned doctor, with the Watson logo standing in for the doctor’s face. Illustration: Eddie Guy

In the past few years, these hospitals contain begun publishing studies about their experiences with Watson for Oncology. In India, physicians at the Manipal Comprehensive Cancer seat evaluated Watson on 638 breast cancer cases and create a 73 percent concordance rate in treatment recommendations; its score was brought down by indigent performance on metastatic breast cancer. Watson fared worse at Gachon University Gil Medical Center, in South Korea, where its top recommendations for 656 colon cancer patients matched those of the experts only 49 percent of the time. Doctors reported that Watson did poorly with older patients, didn’t hint certain measure drugs, and had a bug that caused it to recommend surveillance instead of aggressive treatment for certain patients with metastatic cancer.

These studies aimed to determine whether Watson for Oncology’s technology performs as expected. But no study has yet shown that it benefits patients. Wachter of UCSF says that’s a growing problem for the company: “IBM knew that the win on Jeopardy! and the partnership with Memorial Sloan Kettering would rep them in the door. But they needed to show, fairly quickly, an repercussion on hard outcomes.” Wachter says IBM must convince hospitals that the system is worth the fiscal investment. “It’s really vital that they Come out with successes,” he says. “Success is an article in the New England Journal of Medicine showing that when they used Watson, patients did better or they saved money.” Wachter is still waiting to contemplate such articles appear.

Sloan Kettering’s Kris isn’t discouraged; he says the technology will only rep better. “As a tool, Watson has extraordinary potential,” he says. “I attain hope that the people who contain the brainpower and computer power stick with it. It’s a long haul, but it’s worth it.”

Some success stories are emerging from Watson Health—in certain narrow and controlled applications, Watson seems to subsist adding value. Take, for example, the Watson for Genomics product, which was developed in partnership with the University of North Carolina, Yale University, and other institutions. The tool is used by genetics labs that generate reports for practicing oncologists: Watson takes in the file that lists a patient’s genetic mutations, and in just a few minutes it can generate a report that describes perfect the pertinent drugs and clinical trials. “We enable the labs to scale,” says Vanessa Michelini, an IBM Distinguished Engineer who led the evolution and 2016 launch of the product.

Watson has a relatively facile time with genetic information, which is presented in structured files and has no ambiguity—either a mutation is there, or it’s not. The tool doesn’t employ NLP to mine medical records, instead using it only to search textbooks, journal articles, drug approvals, and clinical visitation announcements, where it looks for very specific statements.

IBM’s partners at the University of North Carolina published the first paper about the effectiveness of Watson for Genomics in 2017. For 32 percent of cancer patients enrolled in that study, Watson spotted potentially vital mutations not identified by a human review, which made these patients grand candidates for a new drug or a just-opened clinical trial. But there’s no indication, as of yet, that Watson for Genomics leads to better outcomes.

The U.S. Department of Veterans Affairs uses Watson for Genomics reports in more than 70 hospitals nationwide, says Michael Kelley, the VA’s national program director for oncology. The VA first tried the system on lung cancer and now uses it for perfect solid tumors. “I attain arbitrator it improves patient care,” Kelley says. When VA oncologists are deciding on a treatment plan, “it is a source of information they can bring to the discussion,” he says. But Kelley says he doesn’t arbitrator of Watson as a robot doctor. “I attend to arbitrator of it as a robot who is a master medical librarian.”

Most doctors would probably subsist delighted to contain an AI librarian at their beck and call—and if that’s what IBM had originally promised them, they might not subsist so disappointed today. The Watson Health memoir is a cautionary tale of hubris and hype. Everyone likes ambition, everyone likes moon shots, but nobody wants to climb into a rocket that doesn’t work.

So Far, Few Successes IBM began its ail to bring Watson into the health suffuse industry in 2011. Since then, the company has made nearly 50 announcements about partnerships that were intended to develop new AI-enabled tools for medicine. Some collaborations worked on tools for doctors and institutions; some worked on consumer apps. While many of these alliances contain not yet led to commercial products, IBM says the research efforts contain been valuable, and that many relationships are ongoing. Here’s a representative sample of projects.

This article appears in the April 2019 print issue as “IBM Watson, Heal Thyself.”


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