1The steady rise of Wikipedia.org and the Open Source software movement has been one of the big surprises of the 21st century, threatening stalwarts such as Microsoft and Britannica, while simultaneously offering insights into the emergence of large-scale peer production and the growth of gift economies.
2Many questions arise when confronted with the streamlined efficacy and apparent lack of organisation and motivation of these new global enterprises, not least “how does this work?” Stigmergic collaboration provides a hypothesis as to how the collaborative process could jump from being untenable with numbers above 25 people, towards becoming a new driver in global society with numbers well over 25,000.
Kurator is a web service which allows you to easily customize one or more visual magazines that automatically aggregate the hashtags, lists, Twitter users, Facebook pages and RSS feeds you specify.
Specifically, you can aggregate from the following sources:
Twitter User
Twitter List
Twitter Keywords
Twitter Hastags
Twitter Mentions
Twitter Top Followers
Facebook Page
Facebook Keywords
RSS Feed
You can also filter and specify specific keywords that you want to be included/excluded.
Kurators offers the ability to title each stream, and to customize somewhat the look of the final magazine by providing a few templates and layouts and access to the controls to adjust the font style, size and color.
The final stream can be published as a web page on Kurator or exported directly to WordPress as a “page”.
Thanks to the excellent work carried out by my colleagues Hemant Purohit and Professor Amit Sheth, we were able to collect 2.7 million tweets posted in the aftermath of the Category 4 Tornado that devastated Moore, Oklahoma. Hemant, who recently spent half-a-year with us at QCRI, kindly took the lead on carrying out some preliminary analysis of the disaster data. He sampled 2.1 million tweets posted during the first 48 hours for the analysis below. Read full post.
FACT: Over half-a-million pictures were shared on Instagram and more than 20 million tweets posted during Hurricane Sandy. The year before, over 100,000 tweets per minute were posted following the Japan Earthquake and Tsunami. Disaster-affected communities are now more likely than ever to be on social media, which dramatically multiplies the amount of user-generated crisis information posted during disasters. Welcome to Big Data—Big Crisis Data.
Humanitarian organizations and emergency management responders are completely unprepared to deal with this volume and velocity of crisis information. Why is this a problem? Because social media can save lives. Recent empirical studies have shown that an important percentage of social media reports include valuable, informative & actionable content for disaster response. Looking for those reports, however, is like searching for needles in a haystack. Finding the most urgent tweets in an information stack of over 20 million tweets (in real time) is indeed a major challenge. Read full post.
In a production economy, value creation depends on land, labor and capital. In a knowledge economy, value creation depends mainly on the ideas and innovations to be found in people’s heads.
Those ideas cannot be forcibly extracted.
All one can do is mobilize collective intelligence and knowledge. If knowing how to produce and sell has become a basic necessity, it no longer constitutes a sufficiently differentiating factor in international competition. In the past, enterprises were industrial and commercial; in the future, they will increasingly have to be intelligent.
The intelligent enterprise stands on three pillars: collective intelligence, knowledge management and information and collaboration technologies and needs the vital energy of intellectual cooperation.
Managing collective intelligence implies a radical change that will naturally elicit a lot of resistance. But we’re talking about a social innovation. Once it is in place, once the resistance has subsided, no one will want to go back to the way it was! As always, the problem lies “not in developing new ideas but in escaping from the old ones.” Keynes.
Local Motors shares its innovations and lets customers be part of the car-building process, while keeping it local.
Last year, I spoke at Shanghai's Radical Design Week about the transformational design of business. I talked about car manufacturing and how, with state of the art 3D fabrication tools, a networked participatory culture and rapid innovation, the car company Local Motors claims to build cars five times faster at one hundred times less the capital cost of conventional manufacturers.
Local Motors is perhaps one of the most comprehensive examples of a revolutionary approach to the design, engineering, manufacturing, sales and marketing of cars. But don't worry if you are not in the automotive industry, the Local Motors story is one about the firms of the future.
Click on Image to Enlarge
Designing high performance organisations
Companies today can change their shape, capability and performance by rethinking and redesigning core processes. In the case of Local Motors, its factories (the Local Motors micro-factory was rated by Jalopnik as one of the world's top 10 most impressive car factories), R&D, sales, marketing and production represents a design system that is an industrial ecology, rather than a series of boxes and silos. Moreover it is much less costly to set up, run and maintain, which enables the company to invest its energies into high quality design and production.
An open networked innovation platform
Local Motors runs competitions to find innovations. For its first vehicle competition, 44,000 designs were submitted and 3,600 innovators shared their knowledge and insights. No one company can hire that many people and there was no cash prize. So, what inspired so many people to participate?
Through its open participatory platform called The Forge, Local Motors has collaborated in automotive innovation with DARPA, the US military research agency, co-designing and building a fully functional prototype of a combat support vehicle in three and a half months. Even large car manufacturers have turned to Local Motors, such as BMW, which is currently running an urban driving experience challenge.
So Local Motors becomes more than just a car manufacturing company – it's an automotive innovation platform and a true community. Local Motors attracts innovators because it is creating and releasing social and intellectual capital into a common pool. This open innovation platform is counter intuitive to many assumptions about how businesses are run, and how intellectual products are created and protected.
I believe there is a historic opporunity to reconstruct a progressive majority around enabling the commons, which would be based on the following political and sociological complementarity between political forces and parties.
Though the fortunes of the new player in politics are down from the moment I wrote this, I believe the general gist is still valid.
Obviously, my proposals are centered on the European situation.
As part of QCRI’s Artificial Intelligence for Monitoring Elections (AIME) project, I liaised with Kaggle to work with a top notch Data Scientist to carry out a proof of concept study. As I’ve blogged in the past, crowdsourced election monitoring projects are starting to generate “Big Data” which cannot be managed or analyzed manually in real-time. Using the crowdsourced election reporting data recently collected by Uchaguzi during Kenya’s elections, we therefore set out to assess whether one could use machine learning to automatically tag user-generated reports according to topic, such as election-violence. The purpose of this post is to share the preliminary results from this innovative study, which we believe is the first of it’s kind.
My colleague Hemant Purohit at QCRI has been working with us on automatically extracting needs and offers of help posted on Twitter during disasters. When the 2-mile wide, Category 4 Tornado struck Moore, Oklahoma, he immediately began to collect relevant tweets about the Tornado’s impact and applied the algorithms he developed at QCRI to extract needs and offers of help.
