A Smarter Planet Blog

Leadership Requires Character Plus Competence

2012-05-16T13:24:26Z

How do leaders develop character and competence ? Dr. Bernard Banks, Colonel in the United States Army and the Deputy Department Head of West Point’s Department of Behavioral Sciences & Leadership, shares his point of view as part of our Next Gen Leaders Series.

Leadership is often equated with simply the act of getting people to do things they otherwise might choose to not to do. while this colloquial definition might suffice in some instances, I think it fails to take into account the complexity associated with exercising leadership.

One problem is that body of literature around leadership has never distilled the phenomena’s definition down to one universally accepted statement. Dr. Peter Northouse’s well-regarded book, Leadership: Theory and Practice (2010), noted that four components are central to all concepts of leadership.

First, leading requires two or more people acting in concert with each other. Second, leading is a process and therefore transpires iteratively over time. Third, leading people involves influence. Finally, leading requires the pursuit of common goals.

Because leading involves people and requires influence, mere competence is insufficient to ensure sustained leadership effectiveness over time. It is critical that leaders possess a solid ethical foundation as well, in order to ensure the results generated through leadership activities can be sustained over time. Character must be integrated with competence.

The Importance of Character

In 1991, General Norman Schwarzkopf gave a famous speech at West Point to the Corps of Cadets. The thesis statement of his remarks was that leaders must possess both competence and character. He asserted that leaders who possess extreme competence, but lack character, can achieve many missions.

However, the manner in which they reach their objectives might place the organization and its people at risk. Conversely, a leader who possesses impeccable character can fail to achieve assigned objectives due to a lack of competence.

Competence and character are required in order to generate sustainable positive outcomes. Yet few private sector organizations consciously integrate character development into their leader development frameworks. The reason lies in the difficulty associated with undertaking such a pursuit.
IBM implicitly pursues character development as part of its leader development process. The company clearly understands the values which serve as the impetus for how it seeks to conduct business.
In the 2011 IBM annual report Sam Palmisano made the following statement. “In the end, it comes back to our values as IBMers. It was no accident that the first major work effort I launched at that senior leadership meeting nine years ago was a collective ‘jam’ on who we are and why we exist. What resulted—the values IBMers themselves shaped—has held up remarkably well as a distillation of what it means to be ‘an IBMer.’”
Having made that statement, how can IBM intensify its character development efforts? The answer lies in behavior.

Integrating character development within a competency-oriented leader development process requires the operationalization of behaviors associated with your organization’s values. All organizations have well articulated values.

Too few companies have translated their values into observable behaviors which are measured throughout the company by managers.

Such behaviors should be categorized by the level of internalization (e.g., resistance, compliance, identification) or skill (e.g., novice, intermediate, expert) they reflect. It is hard to ask a manager to have conversations with their team members concerning character development if they do not have concrete behaviors to reference.

Conversations concerning character can be very difficult ones due to the complexity associated with cultural norms and environmental factors. Empowering managers through the creation of behavioral clarity is important if an organization truly wants to focus on character development as part of its leader development practices.

The greatest risk to an organization usually is not presented by an overabundance of leadership competence. Quite the contrary, the greatest risk lies in a deficit of character amongst its leaders. Organizations who want to embrace both short and long-term perspectives are wise to explore character development as part of their leader development processes.

About the Author
Bernard B. Banks, Ph.D. is a Colonel in the United States Army and the Deputy Department Head of West Point’s Department of Behavioral Sciences & Leadership. Dr. Banks has also led organizations around the globe for over 25 years. He holds a B.S. from West Point, a M.B.A. from Northwestern University’s Kellogg School of Management, a M.P.A. from Harvard University’s Kennedy School of Government, and a Ph.D. in social-organizational psychology from Columbia University.

Technorati Tags: Bernard Banks, IBM, Leadership, Peter Northouse, West Point

Managing Risk: A Global Challenge that Must Be Confronted

2012-05-14T19:56:02Z

Michael Zerbs
Vice President, IBM Risk Analytics

Four years after the worldwide financial crisis began, the recent elections and their aftermaths in Greece and France are fresh reminders that the global financial system and the global economy itself are still fragile. They also teach a powerful lesson about the importance of managing risk.

For many years, investors and regulators viewed government bonds as practically risk-free investments. We now understand how wrong a lot of smart people can be.

Risk exists in all the domains of human endeavor, and, as the financial crisis illustrates, it’s vital for people and organizations to adopt strategies for either reducing risks or understanding them better. This goes for governments, banks, investors and other business leaders alike

In this world of ever-more-complex systems, what is needed is the ability to go beyond the known and explore the unknown. By using technology it’s possible to adopt a holistic view of systems, everything from banking to maritime shipping to retail supply chains, and from that information create realistic scenarios of possible future outcomes of the decisions we make individually and collectively. These scenarios, or models, are the language of risk.

This approach is necessary because it’s often difficult to identify all of the significant risks in a complex system. The financial crisis first shattered our beliefs about supposed low-risk counterparties. It also proved us wrong on “safe” AAA-rated securitized assets. Then we learned that funding would not always be available even for well-regarded financial institutions. Now we face the sovereign debt debacle.

All enterprises face hidden risks. One of the primary reasons for this is they often assess risks business line by business line or function by function. Instead, they should form an integrated view of the entire enterprise, including all assets and liabilities, business strategies going forward and anticipated future investments.

This week at the IBM Vision conference, we will exploring the new technologies that are helping to address risk and solve critical problems that chief financial officers face every day. For example, businesses and government agencies can use analytics software that makes it possible for them to spot fraudulent activity in real time, so they can take action before funds are paid out to crooks.

But, unless it’s managed wisely, technology can cause as many problems as it solves. Too often, business units and functional organizations within an enterprise use different technology tools for assessing risks. That makes it difficult to gain a consistent view of the risk portfolio of the entire firm. The same risk scenario may be interpreted very differently in various parts of the business. A unified approach ensures consistency and gives us confidence that our scenarios are applied in the same way across the board.

Insights are not useful if they don’t lead to action. A unified approach to risk management needs to be complemented with the capabilities to drive action or prepare an action plan.

As the financial crisis has shown, getting enterprise risk right can make all the difference between success and failure for companies–and even countries.

Technorati Tags: IBM, Michael Zerbs

IBM’s Grand Challenges: Pitting Machine Against Man

2012-05-10T21:47:57Z

If you can’t measure progress you can’t make progress. That’s one of the truisms of the human condition. Since the early days of computing, in the 1940s and 50s, computer scientists have searched for ways to measure their achievements. And, often, for all the obvious reasons, they have matched up their machines and software against humans in intellectually rigorous games.

IBM has used the human-versus-machine trope repeatedly over the years to motivate our scientists, focus our research and excite the imagination of the computer-savvy public. It’s one of the ways we take on what we call the “grand challenges”of computing.

Most recently, of course, the TV quiz show Jeopardy! provided the venue for mano-a-maquina combat. IBM’s Watson caused a sensation by beating two past grand-champions. Fifteen years ago, chess was the battlefield. On May 11, 1997, IBM’s Deep Blue became the first computer to defeat a reigning world chess champion in a regulation match. The achievement shocked many people—and showed just how capable computers were becoming.

Murray Campbell, one of three IBM researchers who made up the core of the Deep Blue project, explains why games are so compelling for computer scientists : “If you try to tackle something like general intelligence, you’ll die out of the blocks. It’s too much. But, with games, you focus on one specific problem. You have a chance to make progress and produce something of value that can be used as a component of something bigger.”

Computing pioneer Alan Turing got this whole game thing rolling in 1950 when he published a paper, Computing Machinery and Intelligence, which begins with an immodest proposal: “I propose to consider the question, ‘Can machines think?’”At the time, computers were in their infancy. They were good at calculating, but not much else. In his paper, he mused about creating a written exercise where people would correspond with a computer—and scientists would see if the humans realized that they were interacting with a computer.

Click here to view the embedded video.

This came to be called the Turing Test. To date, no computer has passed it—though you could imagine that IBM’s Watson might do pretty well.

David Ferrucci, who headed up Watson team, considered developing the Deep QA technology to take on the Turing Test, but decided against it. “Part of me would love to do it, but it’s hard to justify. There’s a lot of work and expense. We focused on the business side of things,” he says.

A number of early computer scientists created game-playing programs. One of the most remarkable among them was Arthur Samuel, a pioneer of artificial intelligence who worked for many years at IBM. In the early 1960s, Samuel wrote a checkers-playing program that ran on IBM’s 701 computer. To test his invention, he challenged one of the top US checkers champions to a match—and won. Computer historians credit his checkers work with being one of the earliest examples of non-numerical computation.

Murray Campbell played chess at a fairly high level during his student days in his native Canada. After he arrived as a PhD student at Carnegie Mellon University in 1981, top computer science students like him were beginning to create some really smart game-playing machines. He was a member of two teams that built quite successful chess programs, Deep Thought and Hitech.

By then, computer scientists had realized that they couldn’t beat top human players based on brute force computation alone. Campbell and his colleagues added some additional ingredients: clever search and analysis algorithms along with specialized chips to rapidly search and evaluate chess positions.

Murry Campbell, left, and the Deep Thought team

With that combination they could nearly match the cognitive skills of some of the top chess masters. They did well enough in demonstration matches that IBM hired Campbell and two of his colleagues to come to work at IBM Research. Their task: to build a chess-mastering computer based on the design of Deep Thought. They produced Deep Blue.

Campbell vividly remembers the final game of the six-game match in 1997. Deep Blue was so dominant that the game ended in less than two hours. His wife, Gina, had planned on arriving at the venue in the Equitable Center in New York City to watch the second half, but it was over before she arrived. “It was an absolute crush. It was shocking,” Campbell says. “It made everybody understand in a clear way that problems that may have been considered unsolvable were now solvable.”

These days, Campbell is a senior manager in the Business Analytics and Mathematical Sciences Department at IBM Research. Other members of the department helped create Watson, but Campbell wasn’t involved. (It’s tough working 90-hour weeks for months on end. Plus, he says, “You potentially embarrass yourself in front of the world.”) His focus is applying expertise in optimization, forecasting and probabilistic analysis to IT services—which makes up more than 50% of IBM’s annual revenues.

He’s deeply involved in projects that could transform the way large organizations operate. His work is one of the threads of IBM’s vision of what we call the era of cognitive systems. Because of science and technology breakthroughs, we are beginning to create machines and software that do much more than compute—they sense, learn, predict, and, in some ways, think. This is the ultimate grand challenge. “The hard part of the era of cognitive systems is it’s not a game. We’re transforming the world,” he says.

A Smarter Government Through Cloud Computing

2012-05-11T10:44:23Z

By Andras Szakal
IBM US Federal CTO

A smarter government is more agile, more able to effectively respond to changing government needs and citizen dynamics. One of the best ways to improve the way our government works – both its operational efficiency as well as the services it provides to citizens – is through cloud computing.

Yesterday I participated in the Congressional High-Tech Caucus Cloud Task Force’s “Cloud Computing: A Primer” in Washington, DC as part of an industry panel which tackled issues critical to cloud utilization. The event was designed to help our legislators understand how to optimize IT and lower costs, reducing government waste. I was excited to be able to take this message to Congress, and appreciated the opportunity to join Rep. Michael McCaul (R-TX) and Rep. Doris Matsui (D-CA), co-chairs of the High Tech Caucus.

As citizens, there is a lot of reason to be excited about the promise of cloud computing to help our government operate more efficiently. We like to feel that our tax dollars are hard at work, and that maximum value is being squeezed out of every penny. Rapidly evolving advancements in cloud technologies in such areas as resource pooling, virtualization and operational automation must be considered to help transform and consolidate government data centers to ensure more effective use of resources and lower operational costs.

In today’s cloud-computing model, computing resources can be pooled together like a utility and, just like electricity, can be dialed up in real time, on demand. Cloud computing technologies provide a way for government to consume or deliver technology and government services, more efficiently and securely. Cloud computing, implemented properly, eliminates the risk of becoming locked into an IT system that is difficult and costly to reshape when technological change and new administrative requirements force systems to evolve in both design and implementation. A properly implemented cloud strategy embraces cloud technologies based on open standards and a flexible and extensible operational platform.

At the end of 2010, the Obama administration announced its “Cloud-First” policy, which required agencies to default to cloud-based solutions whenever a secure, reliable, cost-effective cloud option exists. Even before this initiative, the federal government’s exploitation of cloud computing was projected to grow by at least 40 percent over the next several years – a trend that is happening at the local government level too.

Now, the government is making steady progress in executing the reforms outlined in its 25-Point Plan, delivering many ahead of schedule. As this has happened, there has been an expansion in the definition of cloud to include cloud-oriented shared services, which hold great promise for government. Avoiding the redundancy of having each department’s IT shop develop its own software for managing personnel or dealing with public-information requests accounts for nearly half the $932 million in IT savings it has identified through its TechStat program for reviewing IT.

To be sure, cloud won’t be a one size fits all approach when it comes to government implementation. In most cases, a combination of different approaches – private clouds, hybrid clouds and public clouds – should all be examined to determine which approach makes the most sense for the specific need that is being met.

Applications like e-mail, content management, and back-up have been relatively easy to move to the cloud. But using cloud architectures to improve core functions to accelerate development and delivery of business solutions, while reducing duplication of effort will require careful analysis of each application to determine the best migration path.

The coming year is an exciting time for technology leaders in the Federal government) , as new security standards and the move to shared services provide the structure that will help new projects for cost cutting take root, ultimately saving taxpayers money by helping to create a Smarter Government that is more nimble and efficient than ever before.

Technorati Tags: Andras Szakal, IBM

Bringing STEM Learning to Life

2012-05-07T21:40:14Z

by Ioannis (Yannis) N. Miaoulis, president and director of the Museum of Science, Boston

I am delighted that IBM recently launched Minds of Modern Mathematics, the free iPad app that recreates the remarkable 50-foot infographic on the history of math designed by Charles and Ray Eames.

IBM collaborated with the Eameses to develop the richly illustrated timeline for Mathematica: A World of Numbers…and Beyond, an exhibit that opened at the California Museum of Science and Industry (now the California Science Center) in Los Angeles in 1961. Replicas later traveled to the New York World’s Fair and beyond.

Mathematica’s interactive models illustrating basic math concepts have intrigued visitors at the Museum of Science, Boston since 1981. Children like playing with the Celestial Mechanics machine, releasing steel balls into orbits like those of planets around the sun, while a 12-foot-high Probability Board captivates adults, as it sends plastic balls clattering through a maze of steel pins to form a bell-shaped probability curve. Here is our exhibit:

New York Hall of Science director Eric Siegel called Mathematica “a founding document” of interactive STEM exhibitions.

I agree. Mathematica plays an important role in science, technology, engineering, and math (STEM) education because it conveys the wonder and excitement that mathematicians feel doing what they do. In fact, it reflects the Museum of Science’s own hands-on educational approach. We encourage people to practice the skills that scientists and engineers use, instead of simply learning information.

As the language that supports the activities of science, technology and engineering — mathematics performs a timeless function. The more people understand how the S, T, E, and M of STEM relate to each other, the better.

Today, the NY Hall of Science and the Museum of Science, Boston are the only places Mathematica is on public display. A new iPad app expanding the exhibit’s reach couldn’t come at a better time.

U.S. elementary and secondary school students still lag behind many nations on international math and science assessments. K-12 STEM education has focused more on math and science than technology and engineering. But all four disciplines are important.

Since 2004, the Museum has worked to advance knowledge of STEM by integrating engineering as a new discipline in schools and developing technology exhibits and programs via its National Center for Technological Literacy®. Engineering design skills and concepts can engage students in using their math and science knowledge to solve problems, which can lead to the innovation of technologies. Engaging students in identifying a problem, designing a solution, testing, and improving the design also offers a platform for learning in math, science, English language arts, history and social studies.

Meanwhile, if you can’t visit Mathematica at the NY Hall of Science or the Museum of Science and you would like to explore the impact of mathematics on the world since 1000 AD, I urge you to download the free Minds of Modern Mathematics application and interact with this fascinating timeline in a new way.

Technorati Tags: Boston, Eames, IBM, Ioannis (Yannis) N. Miaoulis, Math, Mathematica, mathematics, Museum of Science

Welcome to the Era of Cognitive Systems

2012-05-11T10:26:27Z

By Dr. John E. Kelly III
IBM Senior Vice President and Director of IBM Research

When I was a child, my father worked at General Electric’s research lab in Niskayuna, N.Y. I would visit and watch him tinker with vacuum tubes—light bulb-like devices that were used to direct electrical current in all sorts of gizmos, from radios and TVs to radar and computers. At the time, I didn’t fully understand what he was doing, but those visits inspired me to study science and, ultimately, to get degrees in physics and materials engineering.

I later came to understand that I had witnessed one of the great transitions in the history of technology. While my dad was showing me vacuum tubes, other engineers at GE’s lab were experimenting with the vacuum tube’s successor, the transistor, which ultimately ushered in modern electronics and personal computing. Those core technologies enabled computers that could be programmed to perform a wide variety of tasks.

Today, we are at the dawn of another epochal shift in the evolution of technology. At IBM Research, we call it the era of cognitive systems.

This is a big deal. The changes that are coming over the next 10 to 20 years—building on IBM’s Watson technology–will transform the way we live, work and learn, just as programmable computing has transformed the human landscape over the past 60+ years. You could even call this the post-computing era.

Notice, I don’t use the term “thinking machines.” That’s because I don’t want to suggest that cognitive systems will think like humans do. Rather, they will help us think and make better decisions.

How do we define the era of cognitive systems? It helps to compare it to what came before. The tabulating era began in the 19^th century and continued until the 1940s. Those mechanical devices were used to organize data and make calculations that were useful in everything from conducting a national population census to tracking the performance of a company’s sales force. The programmable computing era emerged in the 1940s when scientists built the first electronic programmable computers. Successive generations of computing technology enabled everything from space exploration to the Internet.

Cognitive systems are fundamentally different. Traditional computers, which are still based on the blueprint that mathematician John von Neumann laid out in the 1940s, are programmed by humans to perform specific tasks. Cognitive systems are capable of learning from their interactions with data and humans—essentially continuously reprogramming themselves. Traditional computers are designed to calculate rapidly. Cognitive systems are built to analyze information and draw insights from it. Traditional computers are organized around microprocessors. With cognitive systems, it’s about the data and drawing insights from it through analytics.

Because of these changes, the machines of the future will do much more than compute. They will be able to sense, learn and better predict the consequences of actions. In the years ahead, machines will cull insights from the vast amounts of information being gathered to help us learn how the world really works, and make sense of all of that complexity, and provide trusted advice to humans—whether heads of state or individuals trying to manage their careers or finances. Computing intelligence will become ubiquitous and pervasive. Increasingly, computers will offer advice, rather than waiting for commands.

This new era of technologies is essential to fulfilling IBM’s goal of using technology to help create a smarter planet—to make the world work better.

We need cognitive systems because recent developments in business, society and technology require new capabilities. The emergence of social networking, sensor networks and huge storehouses of business information create a seeming overabundance of information that some call Big Data. Systems are being asked to find patterns and draw conclusions, often in near real time, from huge quantities of information—and in situations where precise answers are hard to find. At the same time, in fields ranging from retailing to healthcare to government, the individual increasingly stands at the center. People are newly empowered with information about how the world works and able to express themselves in powerful new ways. They’re also becoming increasingly decipherable via the cloud of data that surrounds them and the digital exhaust they leave behind wherever they go. Through data analytics, we can know each other much better.

In addition, we need cognitive systems because some of the fundamental building blocks of traditional computing are crumbling. For instance, consider what’s happening with silicon-based integrated circuits. Today’s microchips are the electronic brains in everything from refrigerators to space ships. However, because of the laws of physics, we are no longer achieving improvements in the performance of microchips that we are accustomed to, and need, using traditional methods. We need to invent new materials and new chip architectures.

As I mentioned before, Watson represent the beginning of the great shift. The system was built by scientists using Von Neuman-based computing hardware—a cluster of 90 servers with a total of 2,880 processors. But Watson’s DeepQA software represents the paradigm shift. It uses a combination of natural language processing and machine learning to understand questions, search for answers in huge databases, and present possible answers rated by its confidence in their accuracy. Watson learns from experience.

Created as a so-called grand challenge by IBM Research, Watson caused a stir last year when it defeated two past grand-champions at the TV quiz show, Jeopardy! Now, IBM scientists are working with experts in healthcare, financial services, government and other domains to create versions of Watson tailored to their needs.

IBM embarks on grand challenges like Watson in efforts to reach ambitious goals with five- to 10-year time horizons. The challenges require significant breakthroughs and, if successful, would represent a revolution in the way we work, create and live our lives.

IBM has long been at the forefront of major advances in technology. IBM was a leader in tabulating machines when four predecessor companies combined in 1911. In 1944, IBM engineers worked with scientists at Harvard University to build an early electromechanical computer, Harvard Mark I, the first machine that could execute long computations automatically. It was the dawn of the computing era. Since then, IBM has been in the forefront of one important computing advance after another.

Just because we’re moving to a new era doesn’t mean the old one is going away. Far from it. Programmable systems will be around for years to come. But the center of innovation is beginning to move to cognitive systems.

We’re on the leading edge of a technology transformation that promises to utterly transform business and society once again. But even though IBM has a broad portfolio of technologies and expertise, no single company can handle this sort of thing alone. We look to our clients, university researchers, students, government policy makers, industry partners and entrepreneurs to take this journey with us. Welcome, all, to the era of cognitive systems.

Technorati Tags: IBM, John Kelly

TweetChat: Using Analytics to Decipher the Mysteries of MS

2012-05-08T13:50:23Z

Multiple sclerosis is a cruel disease. It typically strikes young adults. The body’s own immune system attacks the brain and spinal cord, resulting in physical disabilities, cognitive problems and a host of other chronic symptoms. The cause isn’t known. There is no cure. Fortunately, the amount of biomedical and clinical data related to MS has exploded over the past decade, and, at the same time, new research methods make it possible to assess environmental factors and hundreds of thousands of genetic variations taken from single samples.

Researchers at The State University of New York at Buffalo are using a new approach to computing in an attempt to identify the causes and promising therapies. We’re conducting a TweetChat on Thursday, May 10, from 12 to 1 p.m. ET at Twitter hashtag #IBMDataChat. Please join the conversation about using technology to help defeat MS.

Participants:
·   Shawn Dolley, IBM VP of Big Data Healthcare & Life Sciences (Moderator), @shawndolley
·   Dr. Murali Ramanathan, SUNY Buffalo Professor Pharmaceutical Sciences and Neurology, Director of Graduate Studies & Co-Director, Data Intensive Discovery Initiative, @M_Ramanathan
·   David Smith, Revolution Analytics R Evangelist & VP of Marketing, @revodavid
·   Tim Coetzee, National MS Society Chief Research Officer, @tim_coetzee

Technorati Tags: IBM, MS, Multiple Sclerosis, The State University of New York at Buffalo

Deafness Is No Disability for Master Inventor Dimitri Kanevsky

2012-05-07T13:22:59Z

Like many serial inventors, mathematician Dimitri Kanevsky looks for solutions for problems that he faces in his own life. In his case, some of his biggest challenges are related to the fact that he has been deaf since age 3.

Dimitri Kanevsky demonstrates an Internet-based system for capturing real-time transcripts of teleconferences.

Kanevsky, a member of the speech and language algorithms department at IBM Research, has invented a long string of hearing- and speech-related technologies. They include a system for helping people improve the effectiveness of lip-reading, a method that enables deaf people to converse on the telephone and an Internet-based system for capturing real-time transcripts of phone conferences. “I like to solve challenging problems, and I get a thrill from creating novel math concepts and making discoveries,” he says.

Today, Kanevsky will get another kind of thrill–when he’s honored with a Champion of Change award at the White House. The award recognizes individuals who make a positive impact on science, technology, engineering and math for people with disabilities. Here’s a livestream video link for the event.

While Kanevsky has a long record of achievements as an inventor, including 152 US patents, it’s clear from talking to him that some of his most important inventions may come in the future.

One area where his work could have a significant impact is in turning IBM’s Watson computer into a conversationalist. The brainy computer, which gained fame last year by beating two past-grand-champions on the TV quiz show Jeopardy!, translates text into speech, but, so far, doesn’t understand speech. Potential users of the technology say they would like to be able to converse with the machine in their work settings. Physicians, for instance, envision using IBM Watson as a well-informed adviser when they’re examining and talking to patients.

Kanevsky can help out. He and colleagues at IBM Research have contributed notable improvements to the Baum-Welch algorithm, which is used to make up for shortages of training data in speech-recognition systems. These days, they’re developing new methods for producing more accurate results in situations where people speak with heavy accents or use jargon, or where there’s a lot of background noise. These so-called discriminative algorithms could make possible real-time speech recognition systems that are fully automated and extremely accurate.

Watson is a first step in the emergence of a new era in information technology–which IBM calls the Era of Cognitive Systems. Big shifts are coming in chips, systems, data and programming. Kanevsky believes that these developments offer the promise of major breakthroughs in accessibility technologies–but only if those who architect them take people with disabilities into account from the start. “If this is done in the right way, it will have a tremendous impact on all people with disabilities,” he says.

One of Kanevsky’s earlier inventions, Artificial Passenger, is a system for keeping sleepy drivers awake by telling them stories and jokes, asking questions, and suggesting that the driver take a rest break if it detects that she or he is sleepy. Kanevsky came up with the idea after keeping his wife, Galina, awake by talking to her on a late night drive.

Watson’s a whole lot smarter than Artificial Passenger. For example, Watson can digest the universe of published information about a particular topic and then answer open-ended questions about it. So adding conversational capability to Watson would take speech recognition to a whole new level. Systems can be created that understand both speech and meaning. That’s a worthy challenge for an ambitious serial inventor.

Technorati Tags: Champion of Change, Dimitri Kanevsky, IBM, Watson, white house

Smaller Businesses: They’re Crucial to Saving the Planet

2012-05-04T13:13:04Z

By Wayne Balta
IBM Vice-President for Corporate Environmental Affairs

Ever since then-CEO Thomas J. Watson Jr. made environmental stewardship a company-wide priority in 1971, IBM has been in the vanguard among corporations when it comes to protecting the natural environment. And, with more than 425,000 employees in 170 countries, we can move the needle on sustainability.

But in addition to large companies like IBM, the world’s millions of small and medium-size businesses can also collectively accomplish quite a bit. More than 99% of all businesses fall within the SME category—which is typically defined as organizations with fewer than 500 employees. So, based on sheer scale alone, the world’s SMEs are not only the primary source of innovation and economic growth; they’re also the key to saving the planet.

Many SMEs do outstanding work to further environmental sustainability. They meet or exceed environmental laws and conserve fuel, minimize waste and recycle materials. That’s good, smart business. But I believe that numerous SMEs all around the world also have an opportunity to achieve even more that makes good sense for both their businesses and the environment.

Energy consumption is a good example. At IBM, we have proof that energy conservation is good for us as well as the planet. In 2010, our conservation projects delivered savings equal to 5.7 percent of total energy use. Those projects enabled the company to avoid more than 139,000 metric tons of CO2 emissions. They also helped bolster our bottom line, saving $29.7 million in energy expense.

We drive for continuous improvement. After achieving 12% savings in a test of a new intelligent building management system on our campus in Rochester, Minn., we deployed the system in seven major US locations. This year we’re rolling it out to 17 global locations.

I believe that organizations of any size can benefit from a systemic approach to environmental management. It starts with assessing how the organization intersects with environmental issues to determine what matters. Then set specific goals and lay out a process for making improvements. Give people clear roles and responsibilities, set targets, and to measure progress against them. Then, repeat.

Some improvements can pay off quickly. Switching to higher-efficiency lighting is one example. Other gains can come from better monitoring and maintenance. Faulty valves in an air handling system, for instance, can result in heating and cooling systems running at the same time. If you spot the problem early, you save a lot of money. In other cases, investments in new technologies often pay for themselves sooner than you might guess.

As part of our Smarter Planet dialogue, we’ve said that the way the world works isn’t smart enough to be sustainable. Environmental sustainability is a strategic imperative for businesses. That’s true whether a company has 5 employees or 500,000. If you haven’t yet begun to reap the business benefits from environmental leadership, the time to start is now.

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IBM RECEIVES TOP SUSTAINABILITY AWARD FROM WORLD ENVIRONMENT CENTER--IBM Chairman Samuel J. Palmisano, right, accepted the World Environment Center Gold Medal for International Corporate Achievement in Sustainable Development for IBM's corporate environmental efforts and smarter cities technology at the National Building Museum on Thursday, May 3, 2012 in Washington DC. IBM is the first company to receive the award twice, having also been the 1990 recipient. Also pictured are (L-R) Dr. Terry F. Yosie, President and CEO, World Environment Center, and Ronald Daniels, President, Johns Hopkins University. (Feature Photo Service)

Probing the Mysteries of Multiple Sclerosis

2012-05-09T18:05:03Z

Editor’s note: Please join a Tweet chat featuring Dr. Murali Ramanathan and other healthcare and data analytics experts May 10 from noon to 1 p.m. Eastern Time at #IBMdatachat.

Fortunately, the amount of biomedical and clinical data related to MS has exploded over the past decade, and, at the same time, new research methods make it possible to assess environmental factors and hundreds of thousands of genetic variations taken from single samples.

Researchers at The State University of New York at Buffalo are using a new approach to computing in an attempt to identify the causes and promising therapies. “The eventual goal is to help develop a cure or prevention for MS,” says Dr. Murali Ramanathan, a professor of pharmaceutical sciences and neurology at SUNY Buffalo. “The ability to do this kind of computational analysis is a great complement to basic science and clinical research.”

Ramanathan is one of two researchers leading the Data Intensive Discovery Initiative, or DI2, which is focused on developing new algorithms and modeling techniques for analyzing genetic and environmental factors in multiple sclerosis. They also train graduate students.

He and his colleagues believe that MS is caused by a complex combination of gene-to-gene and gene-to-environment interactions. The chief environmental factors include living far from the equator, viral infections and cigarette smoking. They evaluate a wide range of factors, including gender, geography, ethnicity, diet, exercise, sun exposure and living and working conditions. The clinical data include medical records, lab results, MRI scans and patient surveys.

The researchers are using a computer cluster from IBM’s Netezza that combines processing, a database, storage and analytics into a single system, or appliance. This data-intensive architecture makes it possible to handle large amounts of data and derive insights quickly. The Buffalo researchers have found that analyses that once took days can now be completed in mere minutes.

Previously, Ramanathan used conventional parallel processing on a cluster of computer servers to crunch his data. Those systems divide large problems into smaller ones and solve them concurrently using hundreds or thousands of processors. The Netezza system also takes advantage of parallel processing. But, in addition, it uses specialized processor chips to filter the data that’s sitting in on storage disks before passing along only the relevant pieces to the main processors. At the same time, the system performs some of the analysis as the data is moving off the disks, rather than handling all of it on the main processors. So the design, essentially, moves a lot of the processing to the data rather than moving so much data to the microprocessors. As a result, the work can be done faster and more efficiently.

It took Ramanathan months to warm up to this approach to number crunching, but now he’s a huge fan. “I believe that the vast majority of problems in clinical and biomedical research will be addressed through this new way of computing,” he says.

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