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IBM's Pa. debacle: State wants IBM to pay for $170 million equipment that did not work | Real Questions and Pass4sure dumps

Pennsylvania's effort to get $one hundred seventy million in damages and costs lower back from IBM Corp. for a failed desktop equipment survived its first look at various ultimate month. A dozen preliminary objections from IBM's manhattan and Harrisburg legal professionals have been denied via Dauphin County average Pleas court choose Andrew H. Dowling on Nov. 21. He set target dates for discovery and depositions, with the trial set for 2020, barring further extend.

All that legal artillery will battle to get to the bottom of an initial $one hundred ten million contract, signed with excessive hopes in 2005 under Gov. Ed Rendell, that became supposed to be achieved by 2010. Or 2011, or 2012, under Gov. Tom Corbett, because the deal became amended and delays, bugs, and brought funds proliferated.

Or in no way, because it labored out.

Pennsylvania had its share of previous assistance-know-how disasters in its emergency communications, welfare, and tax places of work, amongst others. The IBM lawsuit, filed by using Gov. Wolf's administration final spring, gives a window into how massive-ticket assistance-expertise contracts can grind on for years, drinking millions of taxpayer bucks, devoid of enhancing public functions.

The state wishes IBM to pay for breach of contract, fraudulent inducement, negligent misrepresentation, nondisclosure, and fraudulent concealment. IBM has called the case "devoid of merit." Its attorneys didn't reply to calls.

The IBM system was purported to tackle payroll deductions and unemployment compensation payments from nearly 300,000 Pennsylvania employers. starting within the Sixties, IBM and different contractors had helped the state custom-build separate tax and compensation systems, in hopes of reducing labor charges and rushing construction.

by means of 2005, youngsters, the state recognized it was saddled with an "inefficient," expensive, and handiest partly automatic legacy of "increasingly outdated, desktop methods," based on the suit. instead of up to date network processing, video-like interfaces and actual-time updates, Pennsylvania nevertheless used mainframe computer systems, eco-friendly-monitor terminals, and day-conclusion record-batching.

So the state department of Labor and industry called on tech contractors to bid on customized-constructing a "complete, finished, built-in and totally functioning" unemployment compensation gadget, meeting 1,500 company requirements, to seamlessly substitute its "mission vital" services through 2010.

IBM pitched difficult: in line with the go well with, the business spoke of its long event with Pennsylvania, its deep pool of skilled application and administration talent, and "wide working talents and potential in the unemployment compensation field" would assure the job bought done correct and on time. In June 2006, the state employed IBM.

however "regardless of being paid almost $a hundred and seventy million," $60 million over funds, and receiving time extensions beyond the customary contact, "IBM in no way delivered," in response to the state.

"IBM had no intention of staffing the project as described," according to the lawsuit. while its salespeople were promising a thorough job, the business's Wall street-mindful administration became relocating forward with plans "to put off thousands of U.S. personnel in desire of outsourcing work to offshore contractors" or "more cost-effective overseas labor." dealing with a brand new generation of tech rivals, IBM "decided that its share price necessary to take priority over customer pride and proper venture start."

as an example, in keeping with the complaint: IBM sent a team of business analysts to start the assignment in 2006. In 2008, with their design complete however not yet applied, the firm pulled them out, growing "an information gap" just as it changed into time to jot down software code and design functions.

soon after, IBM's "every day leads on the challenge" had been moved out. (One, Robert Donaldson, in 2011 grew to be IT chief for the Pennsylvania branch of income.) Untested code piled up. IBM blew previous time limits, then "tried to obscure and whitewash these data with deceptive, intentionally complicated stories."

In 2013, with the IBM assignment  "45 months in the back of agenda and $60 million over finances," the state hired Carnegie Mellon's application Engineering Institute to audit the task. The faculty found "an abnormally high cost of defects" in IBM's incomplete code. Carnegie Mellon advised in opposition t completing the software "on account of the high chance of failure." The state canceled the IBM task and went back to its aged legacy programs.

Pennsylvania is rarely the best state that claims IBM ripped it off. In 2010, Indiana sued IBM after canceling a state welfare-processing contract. IBM sued back. A lessen court advised the state to pay IBM $52 million in 2012. Two years later an appeals courtroom reversed that ruling and pointed out IBM owed Indiana at least $50 million. That case is still in court.

To stay away from future application meltdowns, state officials led by means of Sharon Minnich, the Deloitte Consulting and SAP veteran who heads Pennsylvania's office of Administration below Gov. Wolf', say they've taken steps to standardize and partly centralize state application procurement and overview contracts with additional oversight.

they have got additionally hired a new unemployment-compensation benefits-modernization contractor, Florida-primarily based Geographic solutions Inc.,a one hundred seventy-grownup, 25-12 months-historic deepest company with different state clients. Geographic has agreed to replace that gadget for $35 million, a fraction of what IBM charged. additional mission managers could add up to $10 million.

This time, the contractor expects to purchase solutions already used by using dozens of different states off the rack, as a substitute of building new ones. even if you credit the state's more advantageous management, or the application trade's evolution towards off-the-shelf (or downloaded) commodity items in the 12 years considering the fact that the IBM deal became signed, it appears as if taxpayers may additionally ultimately be in for an IT dividend.

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IBM Accused of Violating Federal Anti-Age Discrimination legislation | Real Questions and Pass4sure dumps

The IBM logo on the entrance to the highlight Towers in Munich, Germany.

ProPublica is a nonprofit newsroom based mostly in new york. check in for ProPublica's massive Story e-newsletter to receive reports like this one for your inbox as soon as they're posted.

a group of former employees has filed a lawsuit in opposition t IBM that accuses the tech big of failing to agree to a federal legislation that requires organizations to expose the ages of people they lay off who're forty or older. The swimsuit, filed in federal district court in manhattan city, additionally alleges that the enterprise has improperly prevented workers from combining to problem their ousters.

it is the second vast prison action in opposition t IBM due to the fact that a 2018 ProPublica story that documented frequent age discrimination by the company in its international restructuring. the former personnel are asking the courtroom to invalidate a written settlement that IBM requires its employees to signal to get hold of severance pay. beneath the document’s provisions, employees comply with hand over any appropriate to problem their dismissal in court docket.

before, most age-connected legal moves contesting an IBM layoff have been brought with the aid of the rare ex-employee who refused to sign the contract and left with out severance. If the district courtroom were to agree that IBM’s separation contract is invalid, it may open the business as much as lawsuits by using tens of heaps of older workers IBM has laid off in fresh years.

today’s lawsuit and the string of other instances filed in the wake of ProPublica’s story face steep odds on account of choices by the Supreme court docket and federal appeals courts that curtailed laborers’ capability to problem employers’ staffing selections. The rationale is to restrict what federal judges view as cumbersome, costly cases that hamstring both employers and the courts.

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“The Supreme court docket is antagonistic to category motion, to collective motion and to employees,” said Cliff Palefsky, a fashionable San Francisco legal professional who frequently represents personnel. “Congress must step in to retain the integrity of its own civil appropriate legal guidelines, now not simply involving age, however additionally race and gender. up to now, it hasn’t.”

ProPublica mentioned in March 2018 that IBM, which had annual profits of $79 billion final yr, had ousted an estimated 20,000 U.S. worker's a while forty and over all over the preceding five years. In some instances, it used funds saved from the departures to employ young replacements to, within the phrases of 1 inner business doc, “appropriate seniority combine.”

the brand new lawsuit follows criminal action remaining fall by means of Boston category-motion legal professional Shannon Liss-Riordan, who filed a class-action case on behalf of 60 ex-IBM employees who had no longer signed the severance settlement and argued they had been discriminated in opposition t on account of their age. In tandem, Liss-Riordan filed rankings of arbitration claims for ex-personnel who had signed the document, which handiest makes it possible for employees to pursue age claims in particular person arbitration hearings.

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The big apple lawsuit opens a brand new felony entrance, difficult the IBM contract’s one-at-a-time restrict as a violation of people’ rights below the federal Age Discrimination in Employment Act. The law permits laid-off people to take prison action against their employers as a bunch, either in court docket or arbitration.

“IBM towards one person isn't a fair fight,” said David Webbert, an Augusta, Maine, legal professional who, in conjunction with his accomplice, Jeffrey younger, and a Washington-based mostly legislation company, filed the brand new case. “IBM towards thousands of individuals who’ve been laid off as a result of their age, that’s a legitimate prison proceeding.

“IBM is petrified of a good fight,” Webbert spoke of.

A spokesman referred to the company had no comment on the newest lawsuit. in keeping with ProPublica’s preliminary findings in 2018, IBM talked about: “we're happy with their enterprise and their employees’ means to reinvent themselves period after period, whereas all the time complying with the law. Their ability to try this is why we're the most effective tech company that has no longer most effective survived but thrived for more than a hundred years.”

the new suit is filed within the name of four ex-employees who have been of their mid-50s once they had been ousted by the company, including Cheryl Witmer of Firestone, Colorado.

Witmer noted in an interview she started her profession repairing IBM Selectric typewriters in 1984 and became a software supervisor in the business’s cloud division in 2016 when she abruptly received a foul job-efficiency ranking and become advised she become retiring.

“however I’m now not retiring,” she pointed out she instructed her manager.

“sure, you're,” she quoted the manager as replying. ProPublica documented dozens of similar employee ousters that began as layoffs however have been transformed to retirements, a transformation that saved down IBM’s layoff counts, where excessive numbers can set off public disclosure requirements.

Witmer referred to she felt she had little alternative but to sign the enterprise’s severance settlement because she necessary the funds while she searched for a brand new job. “I couldn’t find the money for no longer to” signal, she referred to.

examine extra chopping ‘historical Heads’ at IBM because it scrambled to compete in the web world, the once-dominant tech enterprise reduce tens of lots of U.S. worker's, hitting its most senior employees hardest and flouting rules in opposition t age bias.

The ADEA requires that workers over forty who're being laid off be informed the job positions and the a while of the place holders who're being laid off with them so that they can decide even if to pursue an age-discrimination case or to waive the correct to achieve this.

IBM stopped providing this counsel to personnel in 2014 when it rewrote its severance agreement. under the old settlement, departing employees couldn't acquire severance until they agreed to waive their right to pursue legal motion. the new agreement employees have been required to signal to acquire severance did enable them to file claims of discrimination, but best in individual hearings earlier than an arbitrator.

IBM executives seem to have concluded that this exchange in the doc accredited them to stop offering the ages of personnel have been being laid off. legal experts say this made it a good deal tougher to find facts of age discrimination, which requires establishing a sample drawn from tremendous numbers of layoffs.

until the newly filed swimsuit, IBM’s decision to stop disclosing age assistance has no longer confronted felony problem.

it be 2019 and IBM remains trying to find a Use Case for Blockchain | Real Questions and Pass4sure dumps

IBM claims that six banks will challenge stablecoins the usage of its World Wire protocol which is according to the Stellar network. based on the tech colossal, three banks have publicly committed to the initiative, and the the rest will reveal themselves quickly. The announcement joins the resurgence of blockchain razzmatazz this yr that feels eerily similar to the closing time allotted ledger know-how (DLT) was the cat’s meow returned in 2016.

additionally study: Bitcoin’s Social Contract should Be Resilient to the Whims of Future Generations

IBM’s Race to stay important: From the ‘biggest industrial makes use of of Blockchain’ to inner most Banks Issuing Stablecoins

With JP Morgan’s newly announced coin and Digital Asset replacing Blythe Masters with new CEO Yuval Rooz, it looks blockchain hype is steaming up as soon as once more like a warm pile of cow manure. One leading illustration is IBM currently making headlines when it announced that six banks signed letters of intent to supply stablecoins using the IBM protocol called World Wire. The barrage of IBM’s meaningless blockchain announcements over the years indicates the fervor with which the multinational tech firm has tried to continue to be principal. just a few individuals might recollect the disbursed ledger mania three years ago when IBM told the world it deliberate to launch “the largest commercial uses of blockchain” by way of September 2016, returned when everyone became gaga for commercial enterprise blockchains that have produced nothing however media puffery.

despite the fact, in accordance with IBM’s vice president of blockchain technologies, Jerry Cuomo, and a slew of online publications, the system promised in 2016 become allegedly speculated to free one of the vital “$a hundred million in capital tied up at any given time in transaction disputes in the IBM global Financing unit.” IBM even released a blockchain platform available for cloud builders that seems like a cheesy Javascript replica of the NXT blockchain with a unusual VS Code extension. IBM additionally all started the Open Blockchain Initiative and worked with the Hyperledger project that 12 months. however just like the storm of Hyperledger announcements, R3 signing 60 banks, and initiatives like Citigroup’s ‘Citicoin,’ IBM’s greatest industrial makes use of of blockchain seem to be all fluff. while public blockchains like BCH, BTC, and ETH have settled trillions of notional price in a permissionless manner they should still doubtless ask: What have all these company blockchains done in the last few years?

It's 2019 and IBM Is Still Trying to Find a Use Case for BlockchainThe lackluster IBM Blockchain 2.0 beta platform. Media publications are sometimes desperate to file on these so-known as blockchains but rarely examine them and even look on the protocol. Technobabble Blockchains With Zero Innovation

Now IBM is using the Stellar protocol and claims that the “IBM Blockchain World Wire permits economic institutions to clear and settle cross-border payments in seconds.” reading IBM’s World Wire homepage is like having a horrible flashback to 2016, besides the fact that children, where it’s blockchain mania far and wide once more. The usual explanations cited for utilising IBM’s blockchain consist of lowering clearing expenses, low priced pass-border payments, and the ledger reduces loads of time. IBM’s website insinuates that unlike these loopy libertarian bitcoiners, the service provider is inclined to work with the present financial incumbents.

“Bitcoin mania has led some to believe that banks are no longer needed for comfortable international money switch — Banks, besides the fact that children, disagree,” explains IBM’s World Wire web page.

Of course, the commonplace population and even the media publishing IBM’s blockchain fluff pieces about their quest to comfy the globe’s cash transfer device most effective get a gist of what the enterprise wants the public to see. As with most business blockchain tasks, it’s all closed door deals and the media receives a technobabble press free up about some banks the usage of the platform. no matter if it's IBM, Hyperledger, R3, or JPM coin, there’s nothing interesting about these business blockchain options. Yet they read about these vacuous initiatives daily in one of the most well-known crypto and economic news outlets.

It's 2019 and IBM Is Still Trying to Find a Use Case for Blockchain

many of the projects are downright absurd, like when every person acquired excited about Bumble Bee tuna products being demonstrated on the blockchain. private blockchain swaps and monetary banks buying and selling stablecoins add nothing new to the table: do away with the flamboyant wrapped DLT packaging and all you’re left with is a frequent database. Cryptocurrency suggest Andreas Antonopoulos composed a tweet in 2016 that encapsulated the blockchain hype at the time.

‘fundamental banks comprehensive first international transaction the usage of a blockchain’ — You mean, the same component bitcoin has finished a number of million instances?” Antonopoulos mocked.

The bitcoin evangelist continued:

Correction: Banks paid consultants lots to do once what bitcoin does for pennies each day, hundreds of instances, enhanced.

presently there are lots of projects that are permissionless and have an open ledger for all and sundry to assess. simply because a blockchain project is backed by using ordinary massive identify incumbents doesn’t supply it an edge over public cryptocurrency systems that have been around for a decade. The DLT-embellished studies big tech organizations like IBM and banks like JP Morgan churn out are little more than a feeble try to stay crucial.

What do you suppose about IBM’s blockchain attempts over the years and other smartly known companies developing enterprise digital ledgers? let us know within the comments part below.

OP-ed disclaimer: here is an Op-ed article. The opinions expressed listed here are the author’s own. isn't liable for or responsible for any content, accuracy or fine inside the Op-ed article. Readers should still do their personal due diligence earlier than taking any actions involving the content material. is not dependable, directly or ultimately, for any hurt or loss brought about or purported to be brought about by way of or in reference to the use of or reliance on any tips in this Op-ed article.

graphic credits: Shutterstock, Twitter, IBM Cloud, and IBM logos.

At there’s a bunch of free useful functions. as an example, have you ever viewed their tools web page? You can even lookup the trade cost for a transaction in the past. Or calculate the value of your latest holdings. Or create a paper pockets. And much more.

Tags in this taleAndreas Antonopoulos, Blockchain, Blockchain Hype, Bumble Bee Tuna, allotted Ledger technology, DLT, embellished experiences, Hyperledger, IBM, IBM’s blockchain, JPM Coin, N-Featured, inner most Blockchain, Puff Media, R3, Stellar, World Wire Jamie Redman

Jamie Redman is a monetary tech journalist living in Florida. Redman has been an active member of the cryptocurrency group considering that 2011. He has a keenness for Bitcoin, open supply code, and decentralized purposes. Redman has written thousands of articles for about the disruptive protocols rising nowadays.

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How A.I. Is Finding New Cures in Old Drugs | real questions and Pass4sure dumps

In the elegant quiet of the café at the Church of Sweden, a narrow Gothic-style building in Midtown Manhattan, Daniel Cohen is taking a break from explaining genetics. He moves toward the creaky piano positioned near the front door, sits down, and plays a flowing, flawless rendition of “Over the Rainbow.”

If human biology is the scientific equivalent of a complicated score, Cohen has learned how to navigate it like a virtuoso. Cohen was the driving force behind Généthon, the French laboratory that in December 1993 produced the first-ever “map” of the human genome. He essentially introduced Big Data and automation to the study of genomics, as he and his team demonstrated for the first time that it was possible to use super-fast computing to speed up the processing of DNA samples.

Scientists worldwide have built on Cohen’s insights, and Cohen himself, an MD with a Ph.D. in immunology, has gone on to success as a researcher and pharma executive. But a quarter-century later, genomics has yielded few of the kinds of paradigm-changing medical breakthroughs that many of its early innovators hoped for. Today, as chief executive and founder of Paris-based drug startup Pharnext, Cohen is striving to understand why that rainbow hasn’t led to a pot of gold.

“Any protein in the body has many different functions, not only one,” he says, returning from the piano to talk with me, “just as you are a person who has many functions in the population, not just one.” The phenomenon Cohen is describing is “pleiotropy,” the capacity of a single gene to have multiple, seemingly unrelated effects. It is one of the complexities of disease that has repeatedly frustrated medical researchers in their quest for therapies for the most stubborn illnesses.

Cohen not only appreciates pleiotropy’s significance: He believes that Pharnext and other drugmakers may soon exploit it—with a powerful boost from artificial intelligence. By embracing the body’s complexity, and by using A.I. to more methodically analyze and map the way the chain reactions of disease sweep through the body, he hopes to develop combinations of drugs tuned to attack a plethora of medical conditions.

Cohen and his team are also applying A.I. to search for therapies that leverage “repurposing”—combining existing drugs in ways that give them therapeutic powers that each lacks in isolation. Their long-term goal is a drug pipeline that is far more efficient than Big Pharma’s notoriously slow R&D departments—streamlined by machine learning. Cohen’s sleepy gaze widens with enthusiasm when he describes how it’s all coming along. “Très bien,” he says. “Très économique.”

Running in the same race as Pharnext are companies ranging from giants like Google and IBM to startups such as Insilico Medicine, Recursion Pharmaceuticals, and BenevolentAI. All are deeply invested in the tools of A.I., using them to analyze millions of examples of drug and patient data and tease out patterns of significance. But Pharnext, founded in 2007, predates most of those competitors by several years—and has a longer head start when one factors in Cohen’s decades of earlier research in genomics and pleiotropy.

And perhaps most important, Pharnext’s application of A.I. to medical problems over the course of more than a decade has finally reached a critical inflection point. In October, Pharnext reported positive results for a Phase III trial in humans of one of its drug combinations. The compound is PXT3003, a treatment for a neurodegenerative condition called Charcot-Marie-Tooth disease (CMT), a rare disorder for which no cure has been found. The primary cause of CMT is duplication of a single gene, but a whole cascade of bad things ensues “downstream” from that mutation. Schwann cells, which protect nerves, regress into stem cells that don’t do their job. Axons in the nerves begin to die off. Muscles can’t be controlled, and they shrink as a consequence.

According to Pharnext, its Phase III results (which have not yet been peer-reviewed) showed CMT not merely stabilizing under PXT3003 but also being reversed, as cells began regenerating. Previous treatments, Cohen says, had managed only to slow patients’ decline. Under PXT3003, patients showed statistically significant improvement on two measures of disability. Based on those results, in February the U.S. Food and Drug Administration granted Pharnext “fast track” status for that therapy—an accelerated review process, awarded only when the agency thinks a drug demonstrates “superior effectiveness” in treating a serious disease.

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PATHWAYS: To help find therapies, A.I.-driven startups build maps that show how genes, proteins, and the body interact as diseases progress. This illustration depicts Pharnext’s map for CMT, a neurodegenerative condition.


It is, to be sure, only one hopeful step against one rare ailment. Still, technology has shortened Pharnext’s path in ways with promising long-term implications, shaving years off the drug-design timeline. Preclinical testing and clinical trials generally take eight to 10 years, and developing a novel drug completely from scratch can add seven years to the process, sometimes much more. In the case of PXT3003, in contrast, A.I. helped Pharnext select three existing drugs to repurpose: baclofen, a muscle relaxant; naltrexone, used to treat opioid dependence; and sorbitol, a glucose reduction used as a laxative. Because the drugs were already in use, Pharnext could skip the Phase I trials normally required to ensure their safety—and eliminate the “build from scratch” stage.

FDA fast-tracking increases the odds that PXT3003 could be on the market as soon as 2020—and it’s only one of Pharnext’s many projects. The company will soon begin a second Phase II trial of a drug with indications for Alzheimer’s and a first Phase II trial for an ALS therapy, in both cases using a similar repurposed combination.

Just as important: If these experiments succeed, copycats with deeper pockets could follow suit. Kathleen Sebelius, a secretary of Health and Human Services in the Obama administration who is now a consultant and board member for several health care companies, sees the efforts as part of a growing trend to repurpose. “That all leads to the possibility of a lot shorter investment cycle, and potentially a very different pricing point, and lots of possibilities for rare diseases where there just hasn’t been enough of a financial incentive,” she says. “And that has lots of appeal.” Eric Kandel of the Kavli Institute for Brain Science at Columbia University, a winner of the Nobel Prize in physiology or medicine who is an adviser to Pharnext, says that the startup is at the leading edge of the trend, calling its methodology “both original and powerful.” As for whether that approach will catch on widely, Kandel adds, “We should know soon.”

At the dawn of modern genetic research, almost no one anticipated the enormous complexity in the biology of disease. Many researchers thought the genome would be a kind of instruction manual for the body. Pioneers such as Celera Genomics’ Craig Venter and Francis Collins of the National Institutes of Health were celebrated as “gene hunters,” a term that evoked crusaders scouring the globe for that one “silver bullet” gene that would explain—and facilitate a cure for—a given disease.

To some extent, these researchers found real treasure. Geneticist Nancy Wexler, for example, spent years in Venezuela compiling family trees of those affected by Huntington’s disease, a rare, inherited condition. Her work led to the discovery of the mutation in a single gene that predicts whether an individual will contract the condition.

But scientists soon realized that genetic maps were less like an instruction manual and more like the parts catalog you get with Ikea furniture. What’s more, researchers discovered other catalogs that added complex variables to the relationships between genes and disease—for example, the proteome, the proteins encoded by DNA, and the transcriptome, all the nucleic acids that convert DNA into proteins.

The morning-after disappointment has proved wrenching, as researchers learned that complex diseases, such as cancer and Alzheimer’s, didn’t yield to a single gene. (Even Huntington’s, its gene identified, has remained untreatable.) Today, Cohen and others see a link between the obsession with simplicity and a decline in drug discovery. That decline shows itself in the 1-in-10 success rate for FDA approval of new therapies; in spiraling costs for drug development (what a Tufts study recently identified as “the $2.6 billion pill”); and in the soaring prices of the few treatments that break new ground, such as the $475,000 cost of a course of treatment with Novartis’s leukemia drug Kymriah.

More recently, researchers have begun to grapple with biological complexity with the help of the science of networks. That science’s chief evangelist is Albert-László Barabási, a professor at Northeastern University whose 2014 book Linked popularized the notion that network theory can explain numerous fields, from fashion trends to sexual relations to disease. Barabási and others realized that disease is like a bad signal that moves through a network of connections from genes to proteins to cells to tissues, until all these “perturbations” manifest as the familiar symptoms of disease.

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Ads posted in the Paris Metro as part of a public-awareness campaign about CMT. CMT advocates say the disease affects about 1 person in 2,500 worldwide.


Complicated diseases are confluences of numerous effects, because pleiotropy means that any given protein can act at different points in the body. Startups like Pharnext assume that drugs can also be pleiotropic, acting on more than one protein and more than one interaction in the body at the same time. To find a drug combination capable of tackling complexity, the enormous power of machine learning, with its ability to spot patterns in data, must be wedded to a sense of the structure by which disease operates.

This, in turn, has required an evolution in the relationship between computer scientists and biologists. Newer machine-­learning approaches ingest vastly more data and can assemble hierarchies of information that let them go beyond correlation. Still, harnessing these “deep learning” neural networks into a structure that has any predictive power requires some elegant algorithm-building.

Colin Hill, CEO and founder of GNS Healthcare, is one of the builders. His company, based in Cambridge, Mass., has spent 18 years developing a computer system called REFS, which stands for “reverse engineering, forward simulation.” GNS has raised a total of $38 million over the years—from Amgen Ventures, the venture capital arm of the drug giant, along with Celgene and a variety of other investors—to build and fine-tune its models of disease. And in a recent series of trials, first published in 2017 in the medical journal The Lancet, GNS has detailed REFS’s potential when applied to a disease such as Parkinson’s—an ailment in which pleiotropic factors render existing treatments wildly hit-or-miss in their effectiveness.

With Parkinson’s, the network of interactions set in motion by defective genes has a particular shape to it, and the breakdown of motor functioning is the most reliable indication of its progression. Feeding the genetic data of Parkinson’s sufferers and a control group into REFS helped GNS generate over 100 computer models depicting what might be going on as motor function deteriorates. The models can uncover previously unknown genetic mutations that may contribute to the speedup of deterioration.

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But that’s just the first part. GNS has used those findings to create 5,000 different computer simulations of randomized control trials, each aiming to predict how fast the disease would progress with varying approaches to treatment. Such speed-­testing can be vastly more economical than seeking the same result through controlled human trials. And GNS, in partnership with other drugmakers, is now applying similar approaches to treatments for diabetes, ALS, multiple myeloma, and breast cancer, among other diseases.

“We now have the ability to create and construct, on the computer, representations of human patients and their diseases such that they can now probe, drug by drug, care management intervention by care management intervention, and say what treatments work for which patient,” says Colin Hill, CEO of GNS.

The simulation, in other words, is not just finding correlations: It is answering What if questions. What if they had given drug A instead of drug B to patient X? That ability to simulate and answer counterfactuals is a recent arrival in the practice of A.I. It owes its growing importance in large part to GNS’s technology adviser, Judea Pearl, a longtime A.I. researcher and professor of computer science at UCLA. In a popular volume published last year called The Book of Why, Pearl describes how true intelligence ascends from merely noticing patterns, which machine learning does in spades, to being able to express counterfactual reasoning about what would have happened, based on those patterns. Data alone, disconnected from any idea of a mechanism, doesn’t provide real insight. “Data is profoundly dumb about causality,” claims Pearl. Hill puts it more bluntly: “Deep learning is not that deep.”

Daniel Cohen, now 67, spent his childhood in Tunisia’s heterogeneous society of Jews, Christians, Muslims, “living all together in a very elegant and pacific way.” He credits that experience for his taste for “things that are not complicated, but complex.” When he was 9, Cohen’s family immigrated to Paris, where he pursued the piano avidly. He switched to medicine once he realized he might have a greater impact as a scientist than a musician, but the passion has not left him. He has been a guest conductor at the Royal Philharmonic in London and dreams of leading that ensemble in Tchaikovsky’s Symphony Pathétique. “The predisposition to orchestra conductor, CEO, and scientist are all controlled by the same genes,” he jokes.

Parlaying genomics and technology into pharmaceutical success is something Cohen has done before. He was a cofounder of Millennium Pharmaceuticals, a U.S. oncology-drug maker that helped develop the multiple-myeloma treatment Velcade.

Cohen is bullish that Pharnext can be successful with A.I., but he is also aware of the technology’s limitations. Google’s ­AlphaZero, an A.I. program, was able to beat the world’s human masters at the Chinese strategy game Go, without using any prior human knowledge. But as Cohen points out, Go has a finite set of rules, which AlphaZero knew completely. In biology, thanks in part to pleiotropy, the rules are not fully known—and may never be.

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But thoughtfully designed A.I. has enabled Pharnext to build models around the rules that are known and make choices accordingly. Out of the universe of 10,000 known drugs, the company’s discovery model takes in an assortment of 2,000 that are both out of patent and “marketed”—that is, already judged both therapeutically effective and safe enough to be sold to the public.

To develop its CMT drug, Pharnext first spent about a year assembling its network model for the disease—a framework comparable to GNS’s Parkinson’s map, showing how nervous and muscular problems “cascade” from the relevant gene mutation. Based on this mechanism, the computer model arrived at a short list of 57 candidate drugs that addressed various points in the cascade. Pharnext tested those drugs one by one in vitro, generating a shorter list of 22 to be tested in mice, which finally yielded the three-drug combination that went to human clinical trials. The recent positive Phase III results confirmed that the PXT3003 cocktail is acting at various points in the cascade.

Without the A.I. model, many more years of preclinical testing would have been required beyond the three years it took Pharnext, says Cohen. “With 2,000 drugs [to start with], I could produce all possible combinations, a billion possibilities” to test in vitro. That’s a recipe for countless false positives and dead ends—years of frustration, for now forestalled.

Pharnext’s shares, which trade on the Paris stock exchange, have more than doubled since October’s Phase III results announcement. The company has spent about 120 million euros ($135 million) over the past decade on research and development—a very modest figure by pharma standards. It has never made a profit, but analysts estimate that if PXT3003 reaches the market, revenue—9 million euros in 2018—could soar starting in 2020. (GNS Healthcare is privately held and does not disclose spending or revenue.)

Beyond possible victories for investors, the advances at Pharnext and GNS point the way to A.I.’s growing up—and pharmacology along with it. The ability to reason about causality, and to explore counterfactual questions, is a threshold that users of artificial intelligence have long sought to cross. The computer models at these startups are making a foray in that direction as they manage and tame a bewildering number of variables.

Even the underlying definition of disease may evolve. As scientists are learning, these definitions have been overly simplistic. A study in the journal Bioinformatics last year noted that attempts to treat tumors are hampered by the fact that genetic mutations in cancer are “fundamentally heterogeneous”: What appears as one disease, or class of disease, in fact contains few commonalities and many differences from patient to patient. As it becomes clear that what’s happening in any body differs sharply from the “on/off” model of one gene turning on a set of symptoms, technology can help drug developers wrestle with the complexity.

Last, but hardly least, these A.I.-driven efforts offer a glimmer of economic hope. In an era in which the cost of drug development is a daunting obstacle, smart algorithms may someday enable medical stakeholders to derive more value from the trillions of dollars that have already been spent on drug research. In theory, with repurposing, “you don’t need to design [new] drugs,” Cohen avers. “My feeling is that with 50 drugs, they can treat everything.” That would mean changing yet another definition: the meaning of “discovery.”

A version of this article appears in the April 2019 issue of Fortune with the headline “Finding New Cures in Old Drugs.”

Hicham Oudghiri and Marc DaCosta: Data's cartographers | real questions and Pass4sure dumps

How much did the federal government shutdown cost? After 35 days of deadlock, $6,354,845,148 in wages had gone unpaid to 747,573 federal employees. Every second that ticked by added $2,118 to the figure. Some staffers were furloughed; many more worked without pay. At the Department of Homeland Security, for example, some 245,405 employees were unpaid and 32,706 furloughed. At Treasury, 36,309 workers were furloughed and 82,336 worked unpaid. At the Environmental Protection Agency, 52 percent of its staffers worked without compensation.

Getting a handle on the magnitude of this disruption—as well as its granular details—was no small feat. A little-known New York City fintech company named Enigma tabulated the rising costs in real time on a website it threw up called Government Shutdown 2018–2019.

In order to do so, Enigma searched the Office of Management & Budget’s contingency plans for 109 federal departments. It also tapped for average salaries and worker counts for 46 agencies. In all, it combed through 2,000 pages and 2 million spreadsheet rows of federal data to create the tracker. From idea to launch, the effort took three Enigma number crunchers a total of 36 hours.

The shutdown data is available free, but Enigma’s ability to rapidly make sense of multiple disconnected data ­sources, public and private, and create a customisable view of the global economy has attracted some of the world’s leading companies, from BlackRock to PayPal and Celgene, with many clients paying more than $1 million a year for fingertip access to its insights. 

Enigma is the brainchild of Hicham Oudghiri and Marc DaCosta, best friends since they met 16 years ago as undergraduates at Columbia University, where they studied philosophy. Their startup organises information from thousands of sources around the world into a single, fully linked interface.“People know how the internet works and how to log users and serve them cookies to suggest products on Amazon. That problem is solved. What we’re doing is building a model of the real world,” says Oudghiri, 34, speaking from Enigma’s Humboldt boardroom, named after the Enlightenment-era Prussian philosopher. His co-founder, DaCosta, 34, adds, “This isn’t just about a faster microprocessor or better statistics. Enigma is a knowledge graph of what’s going on in the economy.”

Oudghiri and DaCosta’s journey into the field of data mining began after the financial crisis of 2008. DaCosta was doing graduate work in the cultural anthropology of data at the University of California, Irvine, while Oudghiri was managing renewable-energy projects for BCME Bank in Casablanca, Morocco. Both were curious about explaining the world in light of the global disruptions going on. So they reunited and began to organise sets of publicly available data, starting with Federal Aviation Administration flight logs. They soon discovered a trove of valuable information hiding in plain sight—buried in government logs, university research publications, arcane business filings and shipping manifests. If they could collect, scrub, organise and analyse it, they thought, it might produce a near real-time rendering of the macro economy.

In 2011, Oudghiri and DaCosta formed Enigma and went to work amalgamating public data, mostly from government ­sources like the Census Bureau, the FCC, the Federal Election Commission and the IRS, as well as import records from the US Customs & Border Protection agency and building permits, and putting it together as a single source. They also became experts in uncovering complex, hard-to-find information. For instance, using Freedom of Information Act requests, Enigma taps the CBP’s Automated Manifest System to track every container ship that arrives in the US, including the importer and port of call. From the National Fire Incident Reporting System, Enigma retrieves the cause and location of every fire in the US. To cover energy, Enigma leans on oil-well data from the Railroad Commission of Texas, founded in 1891 to establish tariffs.

The firm’s breakthrough year was 2014, when Enigma raised $4.5 million from Comcast, American Express and the New York Times Co and entered the Fintech Innovation Lab, created by Accenture and the Partnership Fund for New York City. There, embedded with banks and Wall Street giants, Oudghiri and DaCosta discovered that their data had immense utility in financial services. Its information could be connected with customer data in banks’ systems, helping them more quickly recognise fraud or businesses and individuals to underwrite. “We walked out with a battle plan,” DaCosta says of the accelerator. They quickly built a software package, complete with colourful knowledge graphs, and put specialised tools into a compliance interface they called Dossier.

To date, Enigma has synthesised 100,000 data sets in more than 100 countries, organised intelligence on 30 million small businesses and accumulated 140 billion points of data on the US population. It has mapped every molecule used by the US pharmaceutical industry, as well as all trials, patent filings and adverse events.

Braininess permeates Enigma’s headquarters in New York’s Flatiron District: Conference rooms are named after philosophers like Michel de Montaigne and Augustine. Bookshelves are filled with scholarly tomes, from coding manuals like Machine Learning for Hackers and Data Quality and Record Linkage Techniques to classics like the Dialogues of Plato, Ulysses and Rousseau’s Confessions. Covering the walls are maps and experimental artwork exploring “the intersection of data and experience”.

But Enigma’s nerdy academic culture hasn’t stopped it from attracting corporate clients and funding from Silicon Valley and Wall Street. BlackRock, PayPal, American Express, MetLife, BB&T, Celgene, Merck and EMD Millipore have signed up. Some $130 million in capital has flowed into the firm in the past seven years from venture investors like NEA, Crosslink Capital and Glynn Capital and hedge funds like Two Sigma Ventures and Third Point Ventures.

Forbes estimates Enigma’s valuation is $750 million, with revenues pushing $30 million annually, the company having doubled its customer base in 2018.

If a hedge fund wants to know which restaurant chains are growing the fastest, Enigma can check FCC logs for radio licences, which are required to open drive-through windows. Insurers use Enigma for risk assessment, and pharma companies query its data-crunching machines to improve drug safety.

Want to quickly and precisely identify the best candidates for small business loans? Instead of cold-calling storefronts or schmoozing local Chamber of Commerce officials, Enigma will synthesise property-tax filing information with state business filings and Uniform Commercial Code liens to come up with automated credit identities. Need to avoid underwriting in risky fire zones? Why not sew together data sets on emergency-call logs and building permits?

At MetLife, chief digital officer Greg Baxter is starting to use Enigma data pulled from public health systems and universities by connecting it to its own systems to detect pockets of illness or risk and to improve underwriting. And in MetLife’s $588 billion investment management arm, Baxter is using Enigma data to quantify how the quality of restaurants, parks and community event spaces affects real estate prices. “They discover data sources, they organise the data and then they find ways through machine learning of connecting the data,” Baxter says. “When you combine that external data with their internal domain data, then you start to get phenomenally predictive and insightful trends.”

Investor John Fogelsong of Glynn Capital thinks the startup could present a threat to legacy closed-system “big box” technology vendors like Oracle, IBM, SAS and SAP: “Each new data set Enigma ingests compounds the company’s ability to improve a customer’s business processes.”BlackRock’s recent experience is illustrative. When Frank Cooper, its newly minted chief marketing officer, was asked to shake up how the $6-trillion-in-assets firm prospects for clients, Enigma made a surprising discovery: Contrary to the traditional approach based on regional and demographic targeting, there was little correlation between a client’s location and his or her preparedness for retirement, but political engagement ranked high. “If someone is politically active and even if they are a renter, they’re much more likely to plan for retirement,” Cooper says. “It was shocking for us.” Not all of Enigma’s machine-learning algorithms are trained on the hot pursuit of profits. The firm has volunteered its data efforts toward studying the gender gap across 558 occupations and has already identified that some of the most egregious disparities occur in accounting, retail and sales.After a November 2014 fire killed five people in a smoke-detector-less home in New Orleans, Enigma worked with the city’s fire department and Office of Performance & Accountability to identify areas where fire safety is weakest.

On Enigma’s public website, it offers national fire-incident data free for city planners to use, and 50 years of weather anomalies in the US, plus nationwide data sets on everything from cancer statistics to so-called adverse events, as defined by the FDA. In New York City, for example, an Enigma FOIA allowed it to synthesise years of rail incident and injury data reported to the Metropolitan Transportation Authority. The firm is also working with a non-profit called Polaris to combat and prevent slavery and human trafficking. The idea is that data can not only lead to better underwriting but also improve government. Oudghiri and DaCosta call it “data for social good”.

Is Enigma vulnerable to the kind of data-usage scandals that have plagued Facebook?

“I think the behaviour from the internet companies has been suspicious if not malicious, creating a picture of your behaviour that is probably above and beyond what you would consent to,” Oudghiri says, arguing that Enigma’s work with financial services firms strikes a more mutually beneficial covenant.

“Your data is a means to understand if you are a legitimate person,” he says. “From a privacy standpoint, you enter those relationships knowingly and explicitly. The need for sharing data arises from a pretty good place.”

(This story appears in the 12 April, 2019 issue of Forbes India. You can buy their tablet version from To visit their Archives, click here.)

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IBM stock tumbles as year-over-year revenue declines for 20th consecutive quarter | real questions and Pass4sure dumps

In the first quarter, strategic imperatives revenue grew 12 percent to $7.8 billion, not adjusted for currency, while cloud revenue grew 33 percent to $3.5 billion. Net income was $2.3 billion.

Still, business is likely to pick up in the second half of this year, as the company wraps up last year's acquisitions, releases new products, closes on large contracts, and moves toward business with better profit margins, executives said on a conference call.

"From my perspective, the quarter played out pretty much how I expected, other than they thought they had a couple more signings relationships in Services that they could have gotten done," Schroeter said in the conference call. "You have to ramp investment ahead of the revenue in order to get that system ready, and that's the period we're in now, and we'll be in through the first half....we also have a lot of opportunities to realize efficiencies in how they run."

IBM's cognitive computing technology, Watson, continued to gain ground over the quarter, with new features for marketing. The company also implemented machine learning into pharmaceutical research, and its property, The Weather Company, announced a new collaboration with Samsung and Lyft.

"The legacy core is a business that we're constantly reinventing," Schroeter said in the conference call.

During the quarter, "cognitive solutions" revenue grew more than 2 percent to $4.1 billion, not adjusted for currency, slightly above the $4 billion predicted by StreetAccount. Revenue from business services and consulting fell slightly, in line with StreetAccount estimates, while sales from items like infrastructure, hardware and operating systems fell slightly short of expectations.

Big Blue has also shifted its hiring focus, amid pressure from the new U.S. presidential administration to hire more Americans. Under Rometty, IBM has pledged to hire about 25,000 U.S. workers in the next four years, including 6,000 in 2017. Many of those are expected to be "new collar" jobs, or jobs that focus on qualifications like apprenticeships rather than traditional higher education.

At least one big investor has pledged his loyalty to the brand: Berkshire Hathaway's Warren Buffett, who has praised IBM's dividends. Berkshire paid an average price of $170.43 per share of Big Blue, as of January. IBM briefly traded above $171 on Tuesday during the regular session, but fell below $164 a share after hours.

IBM said it returned $2.6 billion to shareholders through dividends and share repurchases in the first quarter.

"Now the timing is right, is to get the returns for their investments. If you were to poll 100 IBM executives, it's something that they'd all say, now it's time to get the returns," Schroeter said in the conference call. "We have driven very heavy investment. That has impacted their margins... So yes, it is time for us to get the returns. They have invested quite heavily. The strategy is right. They hear it from their clients every day. The places that they are moving them to and the work they do in the core is highly, highly valuable to them. So I think now, to shorthand that, is yes. Now it's time."

Watch: IBM CFO on H-1B visa program

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Cisco [318 Certification Exam(s) ]
Citrix [48 Certification Exam(s) ]
CIW [18 Certification Exam(s) ]
Cloudera [10 Certification Exam(s) ]
Cognos [19 Certification Exam(s) ]
College-Board [2 Certification Exam(s) ]
CompTIA [76 Certification Exam(s) ]
ComputerAssociates [6 Certification Exam(s) ]
Consultant [2 Certification Exam(s) ]
Counselor [4 Certification Exam(s) ]
CPP-Institue [2 Certification Exam(s) ]
CPP-Institute [2 Certification Exam(s) ]
CSP [1 Certification Exam(s) ]
CWNA [1 Certification Exam(s) ]
CWNP [13 Certification Exam(s) ]
CyberArk [1 Certification Exam(s) ]
Dassault [2 Certification Exam(s) ]
DELL [11 Certification Exam(s) ]
DMI [1 Certification Exam(s) ]
DRI [1 Certification Exam(s) ]
ECCouncil [21 Certification Exam(s) ]
ECDL [1 Certification Exam(s) ]
EMC [129 Certification Exam(s) ]
Enterasys [13 Certification Exam(s) ]
Ericsson [5 Certification Exam(s) ]
ESPA [1 Certification Exam(s) ]
Esri [2 Certification Exam(s) ]
ExamExpress [15 Certification Exam(s) ]
Exin [40 Certification Exam(s) ]
ExtremeNetworks [3 Certification Exam(s) ]
F5-Networks [20 Certification Exam(s) ]
FCTC [2 Certification Exam(s) ]
Filemaker [9 Certification Exam(s) ]
Financial [36 Certification Exam(s) ]
Food [4 Certification Exam(s) ]
Fortinet [14 Certification Exam(s) ]
Foundry [6 Certification Exam(s) ]
FSMTB [1 Certification Exam(s) ]
Fujitsu [2 Certification Exam(s) ]
GAQM [9 Certification Exam(s) ]
Genesys [4 Certification Exam(s) ]
GIAC [15 Certification Exam(s) ]
Google [4 Certification Exam(s) ]
GuidanceSoftware [2 Certification Exam(s) ]
H3C [1 Certification Exam(s) ]
HDI [9 Certification Exam(s) ]
Healthcare [3 Certification Exam(s) ]
HIPAA [2 Certification Exam(s) ]
Hitachi [30 Certification Exam(s) ]
Hortonworks [4 Certification Exam(s) ]
Hospitality [2 Certification Exam(s) ]
HP [752 Certification Exam(s) ]
HR [4 Certification Exam(s) ]
HRCI [1 Certification Exam(s) ]
Huawei [21 Certification Exam(s) ]
Hyperion [10 Certification Exam(s) ]
IAAP [1 Certification Exam(s) ]
IAHCSMM [1 Certification Exam(s) ]
IBM [1533 Certification Exam(s) ]
IBQH [1 Certification Exam(s) ]
ICAI [1 Certification Exam(s) ]
ICDL [6 Certification Exam(s) ]
IEEE [1 Certification Exam(s) ]
IELTS [1 Certification Exam(s) ]
IFPUG [1 Certification Exam(s) ]
IIA [3 Certification Exam(s) ]
IIBA [2 Certification Exam(s) ]
IISFA [1 Certification Exam(s) ]
Intel [2 Certification Exam(s) ]
IQN [1 Certification Exam(s) ]
IRS [1 Certification Exam(s) ]
ISA [1 Certification Exam(s) ]
ISACA [4 Certification Exam(s) ]
ISC2 [6 Certification Exam(s) ]
ISEB [24 Certification Exam(s) ]
Isilon [4 Certification Exam(s) ]
ISM [6 Certification Exam(s) ]
iSQI [7 Certification Exam(s) ]
ITEC [1 Certification Exam(s) ]
Juniper [65 Certification Exam(s) ]
LEED [1 Certification Exam(s) ]
Legato [5 Certification Exam(s) ]
Liferay [1 Certification Exam(s) ]
Logical-Operations [1 Certification Exam(s) ]
Lotus [66 Certification Exam(s) ]
LPI [24 Certification Exam(s) ]
LSI [3 Certification Exam(s) ]
Magento [3 Certification Exam(s) ]
Maintenance [2 Certification Exam(s) ]
McAfee [8 Certification Exam(s) ]
McData [3 Certification Exam(s) ]
Medical [69 Certification Exam(s) ]
Microsoft [375 Certification Exam(s) ]
Mile2 [3 Certification Exam(s) ]
Military [1 Certification Exam(s) ]
Misc [1 Certification Exam(s) ]
Motorola [7 Certification Exam(s) ]
mySQL [4 Certification Exam(s) ]
NBSTSA [1 Certification Exam(s) ]
NCEES [2 Certification Exam(s) ]
NCIDQ [1 Certification Exam(s) ]
NCLEX [2 Certification Exam(s) ]
Network-General [12 Certification Exam(s) ]
NetworkAppliance [39 Certification Exam(s) ]
NI [1 Certification Exam(s) ]
NIELIT [1 Certification Exam(s) ]
Nokia [6 Certification Exam(s) ]
Nortel [130 Certification Exam(s) ]
Novell [37 Certification Exam(s) ]
OMG [10 Certification Exam(s) ]
Oracle [282 Certification Exam(s) ]
P&C [2 Certification Exam(s) ]
Palo-Alto [4 Certification Exam(s) ]
PARCC [1 Certification Exam(s) ]
PayPal [1 Certification Exam(s) ]
Pegasystems [12 Certification Exam(s) ]
PEOPLECERT [4 Certification Exam(s) ]
PMI [15 Certification Exam(s) ]
Polycom [2 Certification Exam(s) ]
PostgreSQL-CE [1 Certification Exam(s) ]
Prince2 [6 Certification Exam(s) ]
PRMIA [1 Certification Exam(s) ]
PsychCorp [1 Certification Exam(s) ]
PTCB [2 Certification Exam(s) ]
QAI [1 Certification Exam(s) ]
QlikView [1 Certification Exam(s) ]
Quality-Assurance [7 Certification Exam(s) ]
RACC [1 Certification Exam(s) ]
Real-Estate [1 Certification Exam(s) ]
RedHat [8 Certification Exam(s) ]
RES [5 Certification Exam(s) ]
Riverbed [8 Certification Exam(s) ]
RSA [15 Certification Exam(s) ]
Sair [8 Certification Exam(s) ]
Salesforce [5 Certification Exam(s) ]
SANS [1 Certification Exam(s) ]
SAP [98 Certification Exam(s) ]
SASInstitute [15 Certification Exam(s) ]
SAT [1 Certification Exam(s) ]
SCO [10 Certification Exam(s) ]
SCP [6 Certification Exam(s) ]
SDI [3 Certification Exam(s) ]
See-Beyond [1 Certification Exam(s) ]
Siemens [1 Certification Exam(s) ]
Snia [7 Certification Exam(s) ]
SOA [15 Certification Exam(s) ]
Social-Work-Board [4 Certification Exam(s) ]
SpringSource [1 Certification Exam(s) ]
SUN [63 Certification Exam(s) ]
SUSE [1 Certification Exam(s) ]
Sybase [17 Certification Exam(s) ]
Symantec [135 Certification Exam(s) ]
Teacher-Certification [4 Certification Exam(s) ]
The-Open-Group [8 Certification Exam(s) ]
TIA [3 Certification Exam(s) ]
Tibco [18 Certification Exam(s) ]
Trainers [3 Certification Exam(s) ]
Trend [1 Certification Exam(s) ]
TruSecure [1 Certification Exam(s) ]
USMLE [1 Certification Exam(s) ]
VCE [6 Certification Exam(s) ]
Veeam [2 Certification Exam(s) ]
Veritas [33 Certification Exam(s) ]
Vmware [58 Certification Exam(s) ]
Wonderlic [2 Certification Exam(s) ]
Worldatwork [2 Certification Exam(s) ]
XML-Master [3 Certification Exam(s) ]
Zend [6 Certification Exam(s) ]

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