Question

In January 2012, Geoff Colvin, a longtime editor at Fortune magazine and a respected commentator on economics and infotech, agreed to play a special game of Jeopardy. The occasion was the annual convention of the National Retail Federation in New York, and Colvin's opponents were a woman named Vicki and an empty podium with the name tag "Watson." Watson's sponsors at IBM wanted to show retailers how smart Watson is. "I wasn't expecting this to go well," recalls Colvin, who knew that Watson had already defeated Jeopardy's two greatest champions. As it turned out, it was even worse than he had expected. "I don't remember the score," says Colvin, "but at the end of our one round I had been shellacked."'

Obviously, Watson isn't your average Jeopardy savant. It's a cognitive computing system that can handle complex problems in which there is ambiguity and uncertainty and draw inferences from data in a way that mimics the human brain. In short, it can deal with the kinds of problems faced by real people. Watson, explains Colvin, "is not connected to the Internet. It's a freestanding machine just like me, relying only on what it knows.... So let's confront reality: Watson is smarter than I am."

Watson is also smarter than anyone who's ever been on Jeopardy, but it's not going to replace human game show contestants any time soon. Watson, however, has quite an impressive skill set beyond its game-playing prowess. For example, it has a lot to offer medical science. At the University of Texas, Watson is employed by the M.D. Anderson Cancer Center's "Moon Shots" program, whose stated goal is the elimination of cancer. This version of Watson, says John Kelly, who oversees the development of IBM's micro-electronics technologies, including Watson, is already "dramatically faster" than the one that was introduced on Jeopardy back in February 2011 (about three times as fast).

Already, reports Kelly, "Watson has ingested a large portion of the world's medical information," and its currently "in the final stages of learning the details of cancer." Then what? "Then Watson has to be trained," explains Kelly. Here's how it works:

Watson is presented with complex healthcare problems where the treatment and outcome are known. So you literally have Watson try to the best diagnosis or therapy and then you look to see whether that was the proper outcome. You do this several times, and the learning engines in Watson begin to make connections between pieces of information. The system learns patterns, it learns outcomes, it learns what sources to trust (emphasis added).

Working with Watson, doctors at the Anderson Center, who are especially interested in leukemia, have made significant headway in their efforts to understand and treat the disease. Watson's role in this process has been twofold:

1. Expanding capacity: It helps to make sense out of so-called big data—the mountain of text, images, and statistics which, according to Kelly, "is so large that traditional databases and query systems can't deal with it." Moreover, says Kelley. big data is "unstructured" and flows "at incredible speeds.... With big data. we're not always looking for precise answers; we're looking for information that will help us make decisions."

2 Increasing speed: Kelley also points out that "Watson can do in seconds what would take people years." The system can, for example, process 500 GB of information—the equivalent of a million books—per second. When it comes to making sense out of the enormous amount of data concerning the genetic factors in cancer, says Kelly; "Watson is like big data on steroids."

Clearly, however, Watson is not replacing "knowledge workers" (doctors) at the Anderson Center. Rather, its being used to facilitate their knowledge work. In this respect, argues Thomas H. Davenport, a widely recognized specialist in knowledge management, Watson is confirming "one of the great cliches of cognitive business technology—that it should be used not to replace knowledge workers, but rather to augment them." On the one hand, even Davenport admits that some jobs have been lost to cognitive technology. In the field of financial services, for instance, "many lower-level" decision makers—loan and insurance-policy originators, credit-fraud detectors—have been replaced by automated systems. At the same time, however, Davenport observes that "experts" typically retain the jobs that call for "reviewing and refining the rules and algorithms [generated byl automated decision systems."

Likewise, human data analysts can create only a few statistical models per week, while machines can churn out a couple of thousand. Even so, observes Davenport, "there are still hundreds of thousands of jobs open for quantitative analysts and big data specialists." Why? "Even though machine learning systems can do a lot of the grunt work," suggests Davenport, "data modeling is complex enough that humans still have to train the systems in the first place and check on them occasionally to see if they're making sense?

Colvin, however, isn't sure that these trends will hold true for much longer. Two years after he competed against Watson, Colvin reported that "Watson is (now] 240 percent faster. I am not He adds that by 2034—when Watson will probably bean antiquated curiosity—its successors will be another 32 times more powerful. "For over two centuries," says Colvin, "practically every advance in technology has sparked worries that it would destroy jobs, and it did.... But it also created even more new jobs, and the improved technology made those jobs more productive and higher paying.... Technology has lifted living standards spectacularly."

Today, however, Colvin is among many experts who question the assumption that the newest generations of technologies will conform to the same pattern. "Until a few years ago: acknowledges former Treasury Secretary Larry Summers, "I didn't think (technological job loss) was a very complicated subject. I'm not so completely certain now." Microsoft founder Bill Gates, on the other hand, is not quite so ambivalent: "Twenty years from now," predicts Gates. "labor demand for lots of skill sets will be substantially lower. I don't think people have that in their mental model."

According to Colvin, today's technology already reflects a different pattern in job displacement: It's "advancing steadily into both ends of the spectrum" occupied by knowledge workers, replacing both low-and high-level positions and "threatening workers who thought they didn't have to worry." Take lawyers, for instance. In the legal-discovery process of gathering information for a trial, computers are already performing the document-sortirg process that can otherwise require smai armies of attorneys. They can scan legal literature for precedents much more thoroughly and will soon be able to identify relevant mat-ters of law without human help. Before long, says Colvin, they "will move nearer to the heart of what lawyers do" by offering better advice on such critical decisions as whether to sue or settle or go to trial.

So what appears to be the long-term fate of high-end knowledge workers? Davenport thinks that the picture is "still unclear," but he suggests that, in order to be on the safe side, would-be knowledge workers should consider reversing the cliché about technology as a means of augmenting human activity: "If there is any overall lesson" to be learned from current trends "it is to make sure you are capable of augmenting an automated system. If the decisions and actions that you make at work are remarkably similar to those made by a computer. that computer will probably be taking your paycheck before long."

Questions:

I. These clays. according to more and more experts. "every worker is a knowledge worker? Consider the definition of knowledge workers in the text: "workers whose contributions to an organization are based on what they know." In what sense might just about any employee qualify as a "knowledge worker"? For example, what qualifies as "knowledge" in an organization's operational activities (that is, in the work of creating its products and services)? What's the advantage to an organization of regarding all employees as knowledge workers?

2. Why are computers, especially cognitive computing systems, so effective in assisting the decision-making process? In particular, how can they increase the likelihood of good decisions under conditions of risk and uncertainty?

3. "The overwhelming message," says Geoff Colvin, seems lo be that no one is safe. "Technological unemployment may finally be here. But even if that's true... it will also be true that, as always, technology is making some skills more valuable and others less so.... Which skills will be the winners?" Colvin supplies one at least one answer to his own question: "it just seems common sense that the skills that computers cant acquire—forming emotional bonds, making human judgments—will be valuable." Thomas Davenport agrees: "It's probably not a bad idea," he suggests. "to improve your human-relationship skills."

Think of a few jobs in which the application of "human-relationship skills" is important—even absolutely necessary. Explain why these jobs require more than just decision-making skills. How about you? Does the job that you want require good human-relationship skills? Do your human-relationship skills need sonic improvement? What sorts of things can you do to improve them?

4. Science journalist Patrick J. Eiger reports that students of the future are likely to have it a lot easier because digital textbooks equipped with artificial intelligence capabilities will guide them along with the patience and perceptiveness of their favorite kindly professors. Take the newly developed Inquire intelligent biology textbook for the iPad. It allows students to stop and type in a question like "What does a protein do? and then presents them with a page full of information specific to whatever concept they're stuck on."

Using "What does a protein do as a model, think of three questions that you would like to ask this book about topics in this chapter. Explain why you chose the questions that you did and what sort of information you'd find helpful in response to each of your questions.

Answer 1

1. A worker in an organization could be considered as a knowledge worker if he/she adds value to the organizagtion. It means if the work or service gives more benefit to the organization the the workers qualifies a knowledge worker. They should have experience and knowledge of the procedures of the company and they should know how to tackle unusual situation with ease. they should be capable to take rational decision and there should be a logic to support their decision. If an organization has a pool of knowledge worker then the organization has a lot of thinking tanks and they will put every effort for the benefit of the company. There will always be more than one solution to the problem as multiple minds will be working on the matter. Also there is another advantage that the problem will be resolved in the lower level itself and it will not be exclated to top level.

2. Computers have the capacity to process huge amount of data. They are designed in that way where they can draw logical conclusion out of the huge volume of data they process. They also process it in a fraction of second. Historical data could be retrved in minutes. So all these factors help in decision making process. Howevery we must keep in mind that these are system which helps us in making decision, they do not take the decision. They present us all the relevant data which we migh skip, but a computer will process all the facts and figures.

3.The statement that technology is making some skills more valuable and some less valuable, this means technology is reducing the time taken for non value addition work. The resource which we are using to get to a conclusion and preparing patterns from data is being done by software. But the skills like people managemnet and team management can not be autometed and we need a human being for emotional quotent. So these skills will always be in the winning seat.

A few jobs where human relationship skills are important is team manager where task has to be distributed based on person's capability and availability. Public speaker where a person has to get emotionally connected with the crowd to make them listen. These jobs require just more than decision making skills because we need to analyse the state of mind in which the people infront of us are. We can not just randomly assign task or deliver talks. We need to acces if the person is willing to do the work, is he/she having any trouble. These tasks ca never be automated, that is why there is always a human intervention necessary even if we develop advanced automated systems.

To develop our human relationship skills we need to be more expressive and polite with people. We need to be connected and have a warm behaviour. All these adds up to the basics where we learned to talk and present ourselves infront of people. We can have behavioural experts to deal with this in organization.

4.based on the model I will ask these three question.

1. What does National Retail Federation does?

   I would like tho know more about the organization, and what it is working on. Who are the founding members and what are the current projects it is handling.

   2. Who are the leaders in cognitive computer system?

I would like to know who are currently providing varios applications of automation and in what field apart from healthcare they are working. Are there any nobel prizes for these kind of work.

3. How does knowledge management model exactly work?

Are there any case studies on knowledge management or have any organization implemented it and got benefit.