Fuzzy searching?

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“Amit Singhai, one of Google’s veteran search algorithm engineers, wants to develop a search engine that second-guesses users’ needs well ahead of time,” reports Paul Marks in New Scientist magazine. “… In future, your Google account may be allowed, under some as-yet-unidentified privacy policy, to know a whole lot more about your life and the lives of those close to you. It will know birthdays and anniversaries, consumer gadget preferences, preferred hobbies and pastimes, even favourite foods. It will also know where you are, and be able to get in touch with your local stores on their websites. Singhai says that could make life a lot easier. For instance, he imagines his wife’s birthday is coming up. If he has signed up to the searching-without-searching algorithm … it sees the event on the horizon and alerts him – as a calendar function can now. But the software then reads his wife’s consumer preferences file and checks the real-time Twitter and Facebook feeds that Google now indexes for the latest buzz products that are likely to appeal to her.”

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What Peter Drucker might have said about the Hon Hai Suicides

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Rick Wartzman, executive director of the Drucker Institute at Claremont Graduate University, wrote an excellent analysis that might shed light on why 10 workers at the Hon Hai Precision Industry plant (which manufactures the new Apple iPad) committed suicide this year. An article which illustrates once again that management is not merely about  scheduling and product lines, it’s also about human relationships and fostering a sense of community. AFter all, work IS life.

Peter Drucker and the Hon Hai Suicides

We will never really know why 10 workers at a Hon Hai Precision Industry plant in China have committed suicide this year and three others there have attempted to kill themselves. Yet their actions are a stark reminder for managers everywhere: The most complicated thing you will ever deal with, by far, is not some elaborate IT system or intricate financial model, but rather the people you must lead and inspire every day.

Work “is impersonal and objective,” Peter Drucker wrote in his 1973 classic, Management: Tasks, Responsibilities, Practices. “But working is done by a human being. … As the old human relations tag has it, ‘One cannot hire a hand; the whole man always comes with it.’ ”

Because of this, Drucker believed, working has five specific dimensions, each of which recognizes that what we do on the job is “an essential part” of our humanity.

First, there is a physiological dimension. “If confined to an individual motion or operation, the human being tires fast,” Drucker pointed out. What’s more, he added, people perform best if they’re able to vary “both speed and rhythm fairly frequently” as they tackle a particular task. “What is good industrial engineering for work,” Drucker concluded, “is exceedingly poor human engineering for the worker.”

In China, some labor activists maintain that the shifts at Hon Hai, also known as Foxconn, are too long, the work is too repetitive, and the assembly line churning out products for Apple (AAPL), HP (HPQ), and others moves too fast. The company, based in Taiwan, has denied these charges. But there is no getting around the fact that all over the world, including in the U.S., Japan, and South Korea, a huge body of research has found that many people are overworked and their physical health is declining as a result.

Knowledge Workers Suffering, Too

This problem isn’t confined to those in factory jobs; knowledge workers are suffering similarly. Late last month, a senior executive at Bank of New York Mellon in London sued the firm for, among other things, allegedly piling on too much work. He had previously complained to his employer that “we are all working … unbearably hard.”

The second dimension of a person at work is psychological. “Work is an extension of personality,” Drucker wrote. “It is achievement. It is one of the ways in which a person defines himself or herself.”

Tellingly, perhaps, a 19-year-old Hon Hai worker who jumped to his death last week from a fifth-floor window of a training center left behind a note indicating that he had “lost confidence” in the future and had become convinced that what he once hoped to accomplish at work “far outweighed what could be achieved.

Although this young man’s reaction to such feelings was obviously extreme, the struggle to find meaning and fulfillment on the job is hardly unusual. Earlier this year, the Conference Board reported that only 45 percent of the Americans it surveyed are happy with their jobs, down from 61 percent in 1987—a long-term slide that the research organization said “should be a red flag to employers.”

The third dimension of working, according to Drucker, is that it provides a sense of community. Even in cases where people have outside activities, he wrote, the workplace is where they find much of their “companionship” and “group identification.”

In the case of Hon Hai, some observers have suggested that the company has grown so quickly, with about 400,000 workers at its sprawling Longhua complex, it has been difficult to forge these social bonds. One news report from Beijing quoted a former employee as saying: The factory “is too big. When I was walking to and from work … I felt helplessly lonely.”

How to Foster Community?

Those employing knowledge workers, meanwhile, face their own challenges on this front, as people have more and more choices about where they live and work and with whom they affiliate. For managers, this pattern leads to a tough question: How can you foster a close-knit community in an age of worker mobility?

Drucker’s fourth dimension of working is that it’s “a living”—”the foundation” of a person’s “economic existence.” In the U.S., Conference Board officials have made a direct link between people’s low job satisfaction and the dual hardship of stagnant wages and high out-of-pocket health-care costs.

China, where income inequality is widening, is now dealing with its own economic strife. A Honda Motor (HMC) transmission plant in Guangdong province resumed normal operations this week after the automaker offered to increase compensation by 24 percent to end a strike there. Also this week, Hon Hai announced that it would boost its workers’ pay by 30 percent. The company stressed that the raise was a response to a labor shortage, not the suicides, but one representative acknowledged that the move could help lift morale.

The fifth and final dimension, Drucker explained, is that there “is always a power relationship implicit … in working within an organization.” In any business, after all, “jobs have to be designed, structured, and assigned. Work has to be done on schedule and in a prearranged sequence. People are promoted or not promoted.” The trick, said Drucker, is to balance this authority with employee participation—to make sure that workers are given an adequate amount of freedom and responsibility.

But this is far from the only trick. Indeed, the thorniest job for any manager is to simultaneously address all of these things: the physiological, the psychological, the social, the economic, and the power dimension of working. The interplay among them, Drucker cautioned, “may be far too complex ever to be truly understood.”

Still, managers must try—with intelligence, sensitivity, and the constant realization that, while there is more to life than work, working is life.

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Rethinking artificial intelligence

David L. Chandler, MIT News Office

The field of artificial-intelligence research (AI), founded more than 50 years ago, seems to many researchers to have spent much of that time wandering in the wilderness, swapping hugely ambitious goals for a relatively modest set of actual accomplishments. Now, some of the pioneers of the field, joined by later generations of thinkers, are gearing up for a massive “do-over” of the whole idea.
This time, they are determined to get it right — and, with the advantages of hindsight, experience, the rapid growth of new technologies and insights from the new field of computational neuroscience, they think they have a good shot at it.

The new project, launched with an initial $5 million grant and a five-year timetable, is called the Mind Machine Project, or MMP, a loosely bound collaboration of about two dozen professors, researchers, students and postdocs. According to Neil Gershenfeld, one of the leaders of MMP and director of MIT’s Center for Bits and Atoms, one of the project’s goals is to create intelligent machines — “whatever that means.”

The project is “revisiting fundamental assumptions” in all of the areas encompassed by the field of AI, including the nature of the mind and of memory, and how intelligence can be manifested in physical form, says Gershenfeld, professor of media arts and sciences. “Essentially, we want to rewind to 30 years ago and revisit some ideas that had gotten frozen,” he says, adding that the new group hopes to correct “fundamental mistakes” made in AI research over the years.

The birth of AI as a concept and a field of study is generally dated to a conference in the summer of 1956, where the idea took off with projections of swift success. One of that meeting’s participants, Herbert Simon, predicted in the 1960s, “Machines will be capable, within 20 years, of doing any work a man can do.” Yet two decades beyond that horizon, that goal now seems to many to be as elusive as ever.

It is widely accepted that AI has failed to realize many of those lofty early promises. “Considering the outrageous optimism of much of the early hype for AI, it is no wonder that it couldn’t deliver. This is an occupational hazard of many new fields,” says Daniel Dennett, a professor of philosophy at Tufts University and co-director of the Center for Cognitive Science there. Still, he says, it hasn’t all been for nothing: “The reality is not dazzling, but still impressive, and many applications of AI that were deemed next-to-impossible in the ’80s are routine today,” including the automated systems that answer many phone inquiries using voice recognition.

Fixing what’s broken

Gershenfeld says he and his fellow MMP members “want to go back and fix what’s broken in the foundations of information technology.” He says that there are three specific areas — having to do with the mind, memory, and the body — where AI research has become stuck, and each of these will be addressed in specific ways by the new project

The first of these areas, he says, is the nature of the mind: “how do you model thought?” In AI research to date, he says, “what’s been missing is an ecology of models, a system that can solve problems in many ways,” as the mind does.

Part of this difficulty comes from the very nature of the human mind, evolved over billions of years as a complex mix of different functions and systems. “The pieces are very disparate; they’re not necessarily built in a compatible way,” Gershenfeld says. “There’s a similar pattern in AI research. There are lots of pieces that work well to solve some particular problem, and people have tried to fit everything into one of these.” Instead, he says, what’s needed are ways to “make systems made up of lots of pieces” that work together like the different elements of the mind. “Instead of searching for silver bullets, we’re looking at a range of models, trying to integrate them and aggregate them,” he says.

The second area of focus is memory. Much work in AI has tried to impose an artificial consistency of systems and rules on the messy, complex nature of human thought and memory. “It’s now possible to accumulate the whole life experience of a person, and then reason using these data sets which are full of ambiguities and inconsistencies. That’s how we function — we don’t reason with precise truths,” he says. Computers need to learn “ways to reason that work with, rather than avoid, ambiguity and inconsistency.”

And the third focus of the new research has to do with what they describe as “body”: “Computer science and physical science diverged decades ago,” Gershenfeld says. Computers are programmed by writing a sequence of lines of code, but “the mind doesn’t work that way. In the mind, everything happens everywhere all the time.” A new approach to programming, called RALA (for reconfigurable asynchronous logic automata) attempts to “re-implement all of computer science on a base that looks like physics,” he says, representing computations “in a way that has physical units of time and space, so the description of the system aligns with the system it represents.” This could lead to making computers that “run with the fine-grained parallelism the brain uses,” he says.

MMP group members span five generations of artificial-intelligence research, Gershenfeld says. Representing the first generation is Marvin Minsky, professor of media arts and sciences and computer science and engineering emeritus, who has been a leader in the field since its inception. Ford Professor of Engineering Patrick Winston of the Computer Science and Artificial Intelligence Laboratory is one of the second-generation researchers, and Gershenfeld himself represents the third generation. Ed Boyden, a Media Lab assistant professor and leader of the Synthetic Neurobiology Group, was a student of Gershenfeld and thus represents the fourth generation. And the fifth generation includes David Dalrymple, one of the youngest students ever at MIT, where he started graduate school at the age of 14, and Peter Schmidt-Nielsen, a home-schooled prodigy who, though he never took a computer science class, at 15 is taking a leading role in developing design tools for the new software.

The MMP project is led by Newton Howard, who came to MIT to head this project from a background in government and industry computer research and cognitive science. The project is being funded by the Make a Mind Company, whose chairman is Richard Wirt, an Intel Senior Fellow.

“To our knowledge, this is the first collaboration of its kind,” Boyden says. Referring to the new group’s initial planning meetings over the summer, he says “what’s unique about everybody in that room is that they really think big; they’re not afraid to tackle the big problems, the big questions.”

The big picture

Harvard (and former MIT) cognitive psychologist Steven Pinker says that it’s that kind of big picture thinking that has been sorely lacking in AI research in recent years. Since the 1980s, he says “there was far more focus on getting software products to market, regardless of whether they instantiated interesting principles of intelligent systems that could also illuminate the human mind. This was a real shame, in my mind, because cognitive psychologists (my people) are largely atheoretical lab nerds, linguists are narrowly focused on their own theoretical paradigms, and philosophers of mind are largely uninterested in mechanism.

“The fading of theoretical AI has led to a paucity of theory in the sciences of mind,” Pinker says. “I hope that this new movement brings it back.”

Boyden agrees that the time is ripe for revisiting these big questions, because there have been so many advances in the various fields that contribute to artificial intelligence. “Certainly the ability to image the neurological system and to perturb the neurological system has made great advances in the last few years. And computers have advanced so much — there are supercomputers for a few thousand dollars now that can do a trillion operations per second.”

Minsky, one of the pioneering researchers from AI’s early days, sees real hope for important contributions this time around. Decades ago, the computer visionary Alan Turing famously proposed a simple test — now known as the Turing Test — to determine whether a machine could be said to be truly intelligent: If a person communicating via computer terminal could carry on a conversation with a machine but couldn’t tell whether or not it was a person, then the machine could be deemed intelligent. But annual “Turing test” competitions have still not produced a machine that can convincingly pass for human.

Now, Minsky proposes a different test that would determine when machines have reached a level of sophistication that could begin to be truly useful: whether the machine can read a simple children’s book, understand what the story is about, and explain it in its own words or ask reasonable questions about it.

It’s not clear whether that’s an achievable goal on this kind of timescale, but Gershenfeld says, “We need good challenging projects that force us to bring our program together.”

One of the projects being developed by the group is a form of assistive technology they call a brain co-processor. This system, also referred to as a cognitive assistive system, would initially be aimed at people suffering from cognitive disorders such as Alzheimer’s disease. The concept is that it would monitor people’s activities and brain functions, determine when they needed help, and provide exactly the right bit of helpful information — for example, the name of a person who just entered the room, and information about when the patient last saw that person — at just the right time.

The same kind of system, members of the group suggest, could also find applications for people without any disability, as a form of brain augmentation — a way to enhance their own abilities, for example by making everything from personal databases of information to all the resources of the internet instantly available just when it’s needed. The idea is to make the device as non-invasive and unobtrusive as possible — perhaps something people would simply slip on like a pair of headphones.

Boyden suggests that the project’s initial five-year timeframe seems about right. “It’s long enough that people can take risks and try really adventurous ideas,” he says, “but not so long that we won’t get anywhere.” It’s a short enough span to produce “a useful kind of pressure,” he says. Among the concepts the group may explore are concepts for “intelligent,” adaptive books and games — or, as Gershenfeld suggests, “books that think.”

In the longer run, Minsky still sees hope for far grander goals. For example, he points to the fact that his iPhone can now download thousands of different applications, instantly allowing it to perform new functions. Why not do the same with the brain? “I would like to be able to download the ability to juggle,” he says. “There’s nothing more boring than learning to juggle.”

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Staffing Challenges Give Birth to Progressive Management

The John’s Hopkins University project the Nonprofit Listening Post, held a Roundtable on the recruitment and retention of professional and support workers at nonprofit organizations. This Sounding demonstrated that overwhelming majorities of nonprofit human service, arts, and community development organizations are facing real challenges recruiting and retaining quality workers; but it also found that most organizations were able to overcome these challenges with adaptive styles of management-employee relations and progressive workstyles.

Five overarching lessons emerged from this conversation:

1) The importance of selling “the context” of nonprofi t jobs;

2) The realization that new, costly methods do not always have better results;

3) The importance of thinking creatively about bringing people into the sector;

4) The need to re-defi ne work and the working environment;

5) The importance of professionalizing the human resource function.

More on this conversation can be found here.

Five overarching lessons emerged from this conversation:
1) The importance of selling “the context” of nonprofi t jobs;
2) The realization that new, costly methods do not always have better results;
3) The importance of thinking creatively about bringing people into the sector;
4) The need to re-defi ne work and the working environment;
5) The importance of professionalizing the human resource function..

Funding Systems Hamper Nonprofit Influence on Policy

The advocacy power of Nonprofits is hampered by the way nonprofits are funded.

The John’s Hopkins University project the Nonprofit Listening Post, hosted a Roundtable on Nonprofit Advocacy and found that America’s nonprofit organizations are widely involved in efforts to influence the public policies affecting them and those they serve, but are constrained by a lack of adequate resources, including tight budgets and limited staff time.

The way funding is garnered and by whom, hampers the organization’s ability to be an independent voice for social policy and progress.  Chief Executive Officer Peter Goldberg (Alliance for Children and Families and United Neighborhood Centers of America) pointed out that in the human service field, nonprofit boards have grown more conservative over the past twenty years. “This creates a tension in our organizations that is sometimes easier to avoid by staying away from policy and advocacy.”

Other participants noted that in light of funding challenges, they feel pressured to stack their boards with wealthy community members who possess strong fundraising skills. Hoping to maximize their chances of attracting such people, they focus their advocacy solely on the organization’s programs and funding shying away from issues that affect their stakeholders, but which will ignite controversy.

Find more on the challenges facing civil society advocacy in the complete report here.

Our question is: What opportunities are emergent in these challenges?

A Simpler Way

Our beliefs about the future determine our attitudes in the present.  Our attitudes in the present determine how we organize ourselves, our families, our communities, our organizations. What we believe is possible determines the limits of what we accomplish.

We invite you to create spaces to question yourself at the level of your beliefs and shift these beliefs to expand the limits of what is possible in your organization and your community.

Our own present beliefs about human organizations and the world in which they come into form are probably best expressed in Margaret Wheatley and Myron Kellner-Rogers book A Simpler Way.

The universe is a living, creative, experimenting experience of discovering what’s possible at all levels of scale, from microbe to cosmos.

Life’s natural tendency is to organize. Life organizes into greater levels of complexity to support more diversity and greater sustainability.

Life organizes around a self. Organizing is always an act of creating an identity.

Life self-organizes. Networks, patterns and structures emerge without external imposition or direction. Organization wants to happen.

People are intelligent, creative, adaptive, self-organizaing, and meaning-seeking.

Organizations are living systems. They too are intelligent, creative, adaptive, self-organizing, meaning-seeking.

What are your beliefs about how the world works?

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