- May 11, 2011, 3:54 PM ET, Gary Hamel Blog
Power has long been regarded as morally corrosive, and we often suspect the intentions of those who seek it. Indeed, the lust for dominion is so unseemly that few of us would openly admit to a craving for clout.
Hence, it might surprise you to learn that one of the world’s most distinguished management thinkers has recently produced a detailed manual for the power-hungry.
It often seems that the mendacious and egotistical have a particular talent for accumulating (and abusing) power—and at some point, most of us have probably been out-maneuvered by a more adept political infighter. But in Power: Why Some People Have it and Others Don’t, Jeffrey Pfeffer, a professor at Stanford University’s Graduate School of Business, gives nice guys and gals the tools they need to even the odds, by summarizing more than 30 years of research and teaching on how to get ahead.
Recently I talked with Pfeffer about why he’s written a book on power at a time when most management gurus are talking about collaboration, community and “open leadership.” Pfeffer’s argument is disarmingly simple: It takes power to get things done. Without power, you’re impotent—irrespective of your talents or the righteousness of your cause. Read the rest of this entry »
Source: Technology Review
IBM researchers are working on systems that can analyze data to tell businesses
exactly what action to take.
As digital data piles up at ever faster rates, the potential is growing for smart algorithms to dig out insights the human brain never could. IBM’s head of analytics research, Chid Apte, directs a team intent on realizing that potential. His group is developing algorithms and other techniques that can extract meaning from data, and it is trying to find ways to use these methods to solve business challenges. Apte talked about his group’s priorities with Tom Simonite, the IT editor for hardware and software at echnology Review.
TR: IBM has been creating and selling analytics products for decades. What’s new?
Apte: Historically, analytics has been about using well-organized past data from inside an enterprise. Now we have two new and different sources of data. One is unstructured data from customer interactions, such as e-mails to support, or call transcripts. The other is social information that we get by tapping into the Web—the world of Twitter and feeds.
My group is working directly with clients to get a better handle on how these sources can be used on the problems businesses are seeing in the trenches.
Can you give an example of such a project and how it can help a business?
We worked with a [consumer packaged-goods] company that makes sports beverages. They were interested in the sentiment—feeling—in the marketplace about their drink. We developed technology to find the exact blogs talking about their product and started extracting the conversations about their sports drink for analysis. We made it possible to judge the sentiment being expressed and also to identify who the influencers are. We want to find the people an enterprise
should target with new messages so the social network will take care of the rest and [the messages] will spread widely.
This technology will form the basis of a new product we will in the future be able to offer all of IBM’s big customers.
Will your analytics technologies interpret more than just numbers?
We have already developed technology that can actually tell you what plan you should execute. It uses techniques called reinforcement learning and Markov decision processes, and we developed a system that uses it with the New York
Department of Tax and Finance. The system automatically generates a plan for dealing with individual tax delinquents. It tells you what to do to maximize the chance of recovery and minimize your costs.
When you train the system, it doesn’t look at the data as a big table; it maps out a directional graph of sequential decisions. From that it can derive the most optimal plan of action.
What about technology like what Watson used on Jeopardy!—technology that would let you pose a question as you would to a colleague?
We see a lot of opportunities for what we call deep QA for business solving. Watson was built primarily by IBM’s natural-language understanding team, but they collaborated with my colleagues very closely for the machine learning involved. We continue to work closely with them.
The basic technology relies on a huge unstructured corpus, like what Watson used. For business, some of the more traditional analytics solutions need bringing together with the deep QA approach, and we are working on that.
What is the biggest challenge to analytics in the near future?
We need a better way to handle large-scale data. Historically it’s the Internet companies that have been out there with petabytes of data, but now it’s moving out into the enterprise in general: telecoms with call detail records, government getting into analyzing large volumes of data, health-care companies pulling together patient records. Instead of analyzing a few dozen factors, we are getting into spaces with hundreds of factors that you need to analyze at the same time.
We’re developing a whole new kind of infrastructure for this world. That includes things like architectures for distributed and parallel machine learning that exploit new hardware. We need to scale up analytics.
Source: The Economist
One of the biggest manufacturers in the world gives 3D printing a go
Waiting for a new print edition
ULTRASOUND scanners are used for tasks as diverse as examining unborn babies and searching for cracks in the fabric of aircraft. They work by sending out pulses of high-frequency sound and then interpreting the reflections as images. To do all this, though, you need a device called a transducer.
Transducers are made from arrays of tiny piezoelectric structures that convert electrical signals into ultrasound waves by vibrating at an appropriate frequency. Their shape focuses the waves so that they penetrate the object being scanned. The waves are then reflected back from areas where there is a change in density and on their return the transducer works in reverse, producing a signal which the scanner can process into a digital image. Read the rest of this entry »
Revealing how Steve Jobs runs Apple is like exposing the secrets behind a magician’s tricks. And several of the magician’s “assistants” just broke their code of silence.
In a lengthy feature titled “Inside Apple,” Fortune magazine’s editor at large Adam Lashinsky paints a clear picture of what it’s like to work at Apple, based on dozens of interviews with current or former employees at the company. In a nutshell: It’s a lot like working for a giant startup with a low tolerance for imperfection. Read the rest of this entry »