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.
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.
Ariana Huffington: “the decision-makers are not acting in the best interests of the public”
Voice Over: “socio-economic evolution out of synch with natural evolution”
Joichi Ito: “frugal engineering happens in the absence of abundance”
Many good endeavors still working in silos. Sharing and cross fertilization not there yet.
Those who have been sideline by power now have ability to by-pass power and connect to all.
Published on Apr 10, 2013
What will the world look like in 50 years? The problems facing our world are so large that they demand disruptive thinking. We don’t have time to think in incremental terms. It’s time to challenge the status quo, and dare to imagine what we can do.
Shortly after the devastating Haiti Earthquake of January 12, 2010, I published this blog post on the urgent need for an SMS code of conduct for disaster response. Several months later, I co-authored this peer-reviewed study on the lessons learned from the unprecedented use of SMS following the Haiti Earth-quake. This week, at the Mobile World Congress (MWC 2013) in Barcelona, GSMA’s Disaster Response Program organized two panels on mobile technology for disaster response and used the event to launch an official SMS Code of Conduct for Disaster Response (PDF). GSMA members comprise nearly 800 mobile operators based in more than 220 countries.
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To connect this effort with the work that my CrisisComputing Team and I are doing at QCRI, our contact at Digicel during the Haiti response had given us the option of sending out a mass SMS broadcast to their 2 million subscribers to get the word out about 4636. (We had thus far used local community radio stations). But given that we were processing incoming SMS’s manually, there was no way we’d be able to handle the increased volume and velocity of incoming text messages following the SMS blast. So my team and I are exploring the use of advanced computing solutions to automatically parse and triage large volumes of text messages posted during disasters. The project, which currently uses Twitter, is described here in more detail.
Phi Beta Iota: Apart from the pioneering and preparatory effort, this is the first time we have seen a reference to a pre-crisis arrangement for crisis mass broadcast to all cell phones providing a code (or multiple codes) for use in populating the crisis map. What this really means is that Dr. Meier has now set a precedent for using SMS to populate a Local to Global Range of Needs (and Fulfilment) Table. This spells the end of the Specialized Agencies (SA) and the Red Cross, among others, as inefficient intermediaries delivering less than 20% of donor dollars to end-needy. The way is now open for a self-organizing system that engages the 80% of the rich that do not donate to charity now (most because they have learned not to trust charities — the Red Cross and Katrina will long be remembered) and can address needs in near real time down to the household level. As the price point of precision-guided micro-parachutes drops, the way is open for chartered flights to literally “rain” manna from the heavens. The work of Dr. Meier and his colleagues is inspiring, and more importantly, a clear break from the past and present inefficient and unresponsive bureaucracies of government and non-governmental organizations.