Drupal cxo on training versus educating

We had a lively debate during the two days of Drupal cxo about Drupal training. The discussion helps a lot to addressing an issue I’ve experienced every since Szeged (Drupalcon 2008) . First of all I would like to thank every one who was involved in the discussions.

From the discussion it became clear that we used training to address two different thinks. There is training as a business opportunity versus training as educating students to have more young people join Drupal. It turns out this is a big difference and we need to address it if we are serious about Drupal internal growth. To make the difference clearer I’ll use “training” versus “educating”. This is neither a battle nor a competition, it is a different target and it requires a different strategy. My interest goes to educating and I think too little awareness exists about what de difference is, why we needed educating and how we can do it.

From a negative spiral of failure to the useful term reverting?

While I consider myself an AI and System & cybernetic researcher, I can’t undo myself from the impression that both domains have failed. There are genius insights that arose from the domains, but the impact on society is marginal. I consider it a failure if we consider the expected impact on society and the current impact.

Today I read a story that again gives me the feeling of failure. There is a nice article in wired about technological evolution: The interview is just a small summery, but I get the impression that much is reinvent the wheel. Accept the mentioning of Stuart Kauffman, I didn’t see any other of the great authors in the summery nor did I read it between the lines.

We fail as a science if people reinvent the wheel, it means we did something wrong, that we didn’t make the insights become autonomous entities in our society (e.g. strong memes). I think the article actually gives us an idea why we failed in respect to the economics of our science. Let me quote a part of the article:

…That was the difference between Tim Berners-Lee’s successful HTML code and Ted Nelson’s abortive Xanadu project. Both tried to jump into the same general space—a networked hypertext—but Tim’s approach did it with a dumb half-step, while Ted’s earlier, more elegant design required that everyone take five steps all at once.

Did we try to do it to good and therefore failed? Do we need dumber half-steps? Notice how in this article the whole idea of variety by natural selection is actually expressed by a term with more negative connotation: “To create something great, you need the means to make a lot of really bad crap.”

Battle plans for the Drupal jungle

It is never easy to communicate about a work in progress. There is always a good excuse to wait a bit longer, so to include the latest news. Still this way I’m never going to post a thing.

Exactly a month ago I was analyzing the Drupalcon sponsoring in the past years to find dedicated Drupal shops. Then I start mailing the companies, most replied fast and were very open to discuss the matter. I’ve gone to Drupalcon with a specific reason: to investigate if open innovation is spontaneous emerging in the Drupal business ecosystem.

While I planed to only interview the Drupal shops, to get a better view on the above research question, we mostly end up discussion my research in more depth. It turns out Drupal is really becoming an open innovation medium, although it was different from my anticipation. I was expecting the companies intentionally manage alliances, but it seems more an emerging issue. It turns out that the IT-support for the open innovation already existed, what a surprise! Another surprise was the diversity, I was expecting to find some patterns, but the diversity is so large it is the pattern!

It is astonishing how big the Drupal project is becoming and I’m not just talking about code. There are all kind of organizations and events happening. The panel discussion can gave a good idea of the diversity of the businesses. From the interviews I’ve also noticed how little redundancy exist, which is even more surprising. Drupal is becoming so big and fit, its not a small ecosystem, it’s a jungle!

Can we bootstrap R&D management and Drupal?

This post is first and for all about my experience on the R&D management conference, but it builds up to a story on Drupal too.

Autonomy Mastry Purpose


Make AI stand for Artificial-or-Aggregative Intelligence


Several different thoughts made me come to the idea to use AI as a way to blur the boundaries between what is artificial and what is aggregative. Lately I’ve come to the impression that artificial is the condition to aggregate intelligence, but it has no direct relation to intelligence itself. Thus AI as used for strong AI, should stand for Aggregative Intelligence. Let me explain the thoughts that got me to this point.

In one of my working papers on the global brain I ran into trouble to find a good abbreviations. The idea is to update IT-alignment by a focus on crowdsourcing. The idea is as old as my research on novelty regulation. We don't need to build intelligence: we can query it. So my first idea was Intelligence Querying, which would become IQ-alignment … ok, that would be a good way to create confusion. If I'm going to create confusion it could be better have a good reason for it. So I decided to got for AI-alignment, where AI stands for Artificial-or-Aggregative Intelligence.

Crowdsourcing works by aggregating amateur intelligence to expert intelligence. One of the experiments of my former advisor created an aggregation from kinder-garden intelligence to adult intelligence. My own novelty regulation model is to get from non-intelligence to some intelligence by aggregation. Indeed, their is only aggregation of intelligence. It doesn’t seem a satisfying answer.

In another working on bootstrapping I’m transforming the notion of Artificial by Herbert Simon to a more general term. He defines Artificial as human made as opposed to spontaneous, but if your investigating the emerging of intelligence, it should become system made as opposed to spontaneous. A system doesn't need to be intelligent to make other parts, not even for self-replication. In this sense what does artificial actually add as value?

We can understand the artificial by looking how we create laboratories to investigate a specific hidden feature: by creating an artificial condition so that the feature comes observable. In our attempt to understand the hidden feature, we mechanize the artificiality to make the feature more robust and autonomous (for more research on this see Bruno Latour). Indeed, artificial intelligence is exactly what we got with expert systems. They are making the artificial conditions to mechanize some intelligent processes. Ironically it seems very improbable that such research will lead to understanding intelligence itself. As intelligence is aggregated.

It may seem as artificial is quit trivial feature, but we don’t see it that way. Artificial is what makes novelties emerge, not just intelligence. Could we say that the artificial is not important? How would the world look if we only know how things work and not have created technology with it? It wouldn’t be satisfying either. We need both, hence I suggest to create a blur by making AI stand for Artificial-or-Aggregative Intelligence. We may say that our brain is the artifact that creates the condition to aggregate intelligence. I like to make a metaphorical gag with it:

Our minds are like whirlpools, while our brains are like toilets. A day is nothing more than a very long flush. To wake up, we flush our brain, and it stops running until we fall asleep.

Global crisis of capitalism and the principle of selective variety, toward an adventures economy.

Professor Manuel Castells gave a talk about the global crisis of capitalism on 28 October 2009, the talk is online. The first hour of the talk is an interesting and detailed outline of the crisis, but not the reason I’m blogging about it. On the last 10 min he gives a vision of the new-new economy. Before the crises we where in a new-economy and now we are in something else.

Investigating innovation processes AS-IS, the methodology

After giving several presentations in September, we have begone to visit companies to investigate how innovation is happening there. The idea is to find the AS-IS situation and compare it to some of the literature. I won't be writing about the specific cases, the companies and the people we work with should be able to evaluate that first. I can talk about the methodology.

19th EIASM doctoral summer school on "open innovation"


Currently my mind still overloaded as is my to-do list, notes and mailbox. It will take a while before I have processed one whole week of non stop idea creation. Let me tell a bit of how the event got organized. In the morning there were presentations by invited professors. In the afternoon three Phd students presented there work. It would take more time than I have at the moment to tell about all the splendid stuff we experienced. Let's just say it would be a lot longer then my last blog to express it all.

The ISPIM experience

A week ago I had the best conference ever, now with the exams and its administration behind me I can finally blog about it. I must say, with the tight schedule (208 presentations in 5 parallel tracks, each having only 15 min presentation time) I was skeptical about the time to discuss the topics. However the breaks and the social events in the evening did provide quite some time to talk. I've connected with some amazing people.


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