From the April 2010 Scientific American Magazine

Problem Solved, LOL

Blog comments point to a new, faster approach in math
By Davide Castelvecchi
In the mid-20th century the encyclopedic works of French mathematician Nicolas Bourbaki traced every mathematical concept back to the subject’s foundations in the theory of sets—the stuff of Venn diagrams—and changed the face of his field. Like many of his notions, Bourbaki existed only in the abstract: he was the pseudonym for a tight-knit group of young Parisian researchers. The Internet-age version could be D.H.J. Polymath, another collective pseudonym who could define a new style of mathematics.

This is another example of an expertise network in action. Also, an excellent example of why collaboration matters. If this works for mathematicians, why not for others? Why is this just a ‘faster approach for math’?

~~ attributed to Benjamin Franklin

“… created what he called advanced chess, in which players are assisted by off-the-shelf software. Each competitor enters the position of their pieces into a computer and uses the moves that the program recommends to inform their decisionmaking.

At a “freestyle” online tournament in 2005, where any kind of entrant was allowed, such human-machine pairings were absolutely awesome. In fact, the overall winner wasn’t one of the grandmasters or supercomputers; it was a pair of twentysomething amateurs using run-of-the-mill PCs and inexpensive apps. …”

Clive notes that

The most brilliant entities on the planet, in other words (at least when it comes to chess), are neither high-end machines nor high-end humans. They’re average-brained people who are really good at blending their smarts with machine smarts.

And …

As we face that trade-off, figuring out how to integrate machine intelligence into our personal lives becomes the key challenge. When should you rely on online tools to fill you in on the news or your friends’ lives? When should you forage on your own?

There’s no one answer — and there never will be, because everyone is different. It’s a personal quest. But there’s also no avoiding the question, because it’s clear that serious cognitive advantages accrue to those who are best at thinking alongside machines.

Ultimately, the real question is, what sort of cyborg do you want to be?

This article is worth reading. It leads to the conclusion that the answers to performance (personal or organizational) lie not in ‘either | or’ trade-offs but rather in how we combine the power of machines and humans.

I remember Steve talking about one thing that made the CCs a team – their willingness to share what they knew and learned each and every day. You see, downhill skiing was (and in someways today still is) viewed as an individual sport not a team sport. The sport was dominated by Europeans. The Swiss and Austrian skiers of that time saw each other, including the teammates from their own countries, as competition. The CCs changed the way ski teams operated. Using technology (two way radios) the skiers that went down the hill ahead of you would call back up the hill with reports of the specific conditions a teammate was going to face.

As Steve puts it in the biography show, it was like saying “here’s how to run the course better – here’s how to beat me.” This collaboration contributed to their success on the world stage and accelerated their climb towards the top of the competitive field. Collaboration allowed them to accelerate their learning as a team and to change their pace of learning; out-pacing the competition. This helped them climb quickly through the ranks of world class skiers.

In our organizations today, sharing knowledge like this could greatly enhance our performance; even when we are in similar competitive positions. What if we turned our solo practices into team like the CCs did? What if we were as selfish?

It seems that a number of physicians in this health network were not keen on the new egalitarian roles presented by the hospital network. Physician roles (indeed all health provider roles) are key to any successful integration across the system. It is not sufficient to mandate integration in IPC.

In retrospect, with SARS, there was chatter on discussion boards that pointed to a problem in Asia that was puzzling health care workers. While there was no name for the problem at the time, the discussion was largely about symptoms and the associated problems. Interestingly, the response to SARS required an unprecedented collaboration between public health resources around the globe — another KM challenge.

A virtual network of epidemiologists brought together public health institutions, ministries of health, and WHO Country Offices to analyze the spread of SARS and to define appropriate public health measures. (Learning from SARS: Preparing for the Next Disease Outbreak — Workshop Summary, 2004, p. 44)

So, lessons from the SARS experience now influence how we respond to global health issues in a flat, interconnected world. Here is the online version of the book referenced above:

And here is a paper on SARS and the KM opportunity for public health — From SARS to systems: developing advanced knowledge management for public health. [Stud Health Technol Inform. 2004;100:149-56.]

Back to swine flu. Can information sources be used to predict an outbreak like swine flu?

Google’s flu trends uses a number of terms, in aggregate, to spot trends in flu enquireies on their site. They even developed a version specific to the Mexico outbreak. The spike on this chart in January and February precedes the recognition by public health authorities.

Similarly, Veratect, a small company in Washington state [@Veratrect on Twitter] claims to have spotted this flu outbreak and notified a number of its corporate clients in advance of the general public health warnings. They were even accused of not reporting this information to Mexico authorities.

The Associated Press reports …

Veratect Inc., a 2-year-old company with fewer than 50 employees, combines computer algorithms with human analysts to monitor online and off-line sources for hints of disease outbreaks and civil unrest worldwide. It tracks thousands of “events” each month — an odd case of respiratory illness, or a run on over-the-counter medicines, for example — then ranks them for severity and posts them on a subscription-only Web portal for clients who want early warnings.

The idea fueling Veratect and similar companies is that blogs, online chat rooms, Twitter feeds and news media and government Web sites are full of data that public health agencies could use to respond faster to problems like outbreaks of swine flu.

Detecting public health outbreaks and even foiling terrorism has become more and more an information problem. Data scattered on the web can be turned into information and ultimately knowledge with the right tools and lenses. Knowledge pattern recognition has become one of the great challenges for today’s organizations and for society.

Addendum (May 26, 2009): Here is another related article from Wired — This article refers to Veratect and also HealthMap.

As I watched this story, I was reminded how even biology today is an information science. The importance of data and information to generating knowledge about this virus is underscored in stories such as this.

The deliberate and systematic coordination of an organization’s people, technology, processes, and organizational structure in order to add value through reuse and innovation. This value is achieved through the feeding of valuable lessons learned and best practices into corporate memory in order to foster continued organizational learning.

The definition I prefer, however, is shorter and to the point – and partly courtesy of Bryan Davis. When asked “what is KM?” I usually respond “knowledge management is about making optimal use of an organization, group or individual’s knowledge assets“. While this is a simple definition, I like the notion of optimization (or as the Comprehensive Auditing Foundation frames it — this is about the three “E’s”: efficiency; economy; effectiveness). I also like the concept of “knowledge assets” which at it’s broadest includes people (human capital), the stuff that stays behind when everyone leaves on the elevator at the end of the day – things like software, hardware, databases, and intellectual property (patents, copyrights, trademarks) (structural capital), and relationships (relationship/social capital).

Feel free to use this definition anytime.

Enjoy.

Permalink: http://www.joelalleyne.net/2009/02/16/kmdi-alumni-presentation-january/

SlideShare link: http://www.slideshare.net/jalleyne/how-can-studying-knowledge-media-design-help-you-with-a-career-in-km?type=presentation.

Joel