Image via Wikipedia Fantastic rap on youtube that explains all about the large hadron collider.

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Image by Getty Images via Daylife Nowadays there is this discussion of will the big CERN experiment annihilate our planet ?
(New York Times , Risk evaluation forum, opinion dominion, …)

Here I found this description of the string theory with nice illustrations.

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Book cover via Amazon More then a decade after his book Out of Control, Kevin Kelly writes this post on the antagonism of swarm intelligence and central control. With the example of Wikipedia, the key example of the swarm intelligence of the web. It is striking, that even wikipedia has a sort of central control, an elite at its center. This proves that central control is needed, but also that it emerges from within the swarm. Quote from the comment of Gary Stein : “the Wikipedia elite was not elected. They simply emerged.”
The post on lifeblog on the same subject is also worth reading.

But what does this all mean ?

We know that swarm intelligence is the result of evolution. According to Kevin Kelly evolution is to slow, so some central control is needed to speed things up. I think he misses the point. It is not the speed that is all about, but the direction.
Evolution is the result of organisms adapting to changing environments. And with environments, I mean environments in its largest meaning : everything outside (and even inside) the organism. All the other individuals of the same species, all the individuals of other species (plants, animals, bacteria, viruses), all the the inanimate matter, air composition … Indeed : everything.
And what is the role of that “everything” in evolution ? It shows direction. Directly or indirectly, it drives organisms to adapt. Example : If climate becomes cooler, animals of a same species become larger (better volume/surface ratio and hence less heat loss). If you provide a forest with nestboxes for decades, the box-inhabiting birds become smaller, since they do not have to fight any longer for holes (the biggest wins the fight) as there plenty of them. Etc.
Without these environmental factors, evolution knows no direction.

The same with swarm intelligence in web 2.0 . If you have something like wikipedia, there are from the start already some dedicated people who want wikipedia to be a high quality encyclopedia. As they are the most dedicated, they will do the necessary effort to give direction to the swarm. They will give negative feedback on lousy topics. They are the evolutional force that guides the rest of the swarm towards quality.

The same happens in a knowledge enterprise. People can act as a swarm, doing whatever the thing they need to do. But unless they all know exactly what “the company” wants, they need some central control to point all noses in the same direction.

So the lesson for management is really simple : do not tell your people how to do their job, but tell them often and very clearly what it is the company wants to do. Reward every act, project, initiative that helps the company forward in the desired direction and correct every behaviour that forms an obstacle to the goals of the company. And when there are several levels of management, make sure every one of them tells the same thing and even more important : is an example of the “good” behaviour.
The swarm will do the rest.

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• We Are The One

(excellent talk by Kevin Kelly)

Ever had a complex decision to make ?

We all make decisions all the time. Right now I am making the decision to keep on writing on making decisions.

We make a lot of decision we are even not aware of, like scratching some place that itches, looking at something, laughing when someone thells a joke …
Than there are a lot of simple decisions. In a bar, pub : what should I drink? Beer ? Wine? Water ? Which shoes should I buy ? this ones or the other ones ?
And then there are the tougher ones,
like investing : should I sell or should I by some more stocks?
like buying a house : this one on the country, but far from public transportation, or that one in town ?
like having another child
like quitting your job for another one
…

And than there are also the really tough ones : like deciding which Software development tool to buy for a development department of 500 people.

For the simple ones the solution is also simple : decide on gut feeling. And if you cannot decide there are two options :

1. it does not matter, for if you cannot decide it means the two choices are equal
2. act as if you chose one of the options but focus on how you feel about missing the not-chosen one. Often a different feeling will show up depending on which one you did not chose, making it a lot more easy to choose.

Difficult decisions are … well … more difficult. But fortunately there are some tools or methods to help you out.

First there is what is called “even swap”.
The principle is the following :

1. list all relevant criteria
2. compare the value of each criterion between the possible choices
3. eliminate criteria that are dominated by the others, and hence not really matter
4. replace (“swap”) one characteristic for another one by giving them sort of an exchange rate whereby you feel that amount x of characteristic X has an equal worth of amount y of characteristic Y (“even swap”)
5. if you keep on doing that, finally you will come up with only one characteristic and a value for choice 1 and another value for choice 2. This makes it easy to decide.

You can find a online software: smart-swaps that support the even swap process developed at the Systems Analysis Laboratory from the
Helsinki University of Technology and a powerpoint presentation about it.

The most elaborated is the Analytical hierarchy process
I will not go in detail about it but the basis is this : you do an top down decompositon of every characteristic that matters (ex.user interface, with at the underlying level : ease of use, artistic qualities, response times …). Than you (best seek agreement with a handfull of people) compare every bottom characteristic pairwise between each possible choice indicating if they are equal for that characteristic, if A is a bit, a lot, very much better or worse than B…).
For complex decisions it means a lot of work, but at the end, and with the nececessary mathematics you get a calculated solution that should be correct… if you did not forgot some characteristic.

As bonus : an interesting article on decision experiments : Don’t sleep on it!

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List of KM-list : version 2 = version 1 with 9 additions

Lucas Mcdonnel 26
Lucas Mcdonnel 50
Knowledge management resource center
Intangibles/KM web sites
Gurteen knowledge web site
Decision support systems (part of the list is on KM
Unspun Best KM websites
Association of collega and research libraries
EPSS Central
KM and organizational learning resources
Birmingham City University
knowledgecog
SLA Toronto chapter
Manitoba quality network
Portals and KM
Maureen Clements
Law and technology resources for legal professions
Luis Suarez
American Library Association
Stan Garfield
Knowledgepoint
248 ways to enhance or share your knowledge
76 ways to build and visualize data
100 Companies That Matter in Knowledge Management 2008
Open Source Knowledge Management Solutions Written in Java
Knowledge Management Reading List
Open Directory Project : Knowledge management
Prism Legal : Knowledge management resources
KmWiki : a collaborative persistent ‘conversation’ on all matters related to knowledge management
Jack Vinson : More knowledge management blogs
David Skyrme : KM Tools
Bill Ives on Blogbridge : KM blogs which he recommends

The intelligent Universe by James Gardner (“provocative and prescient”, “intellectual journey of the year”).
Finally I finished reading it !
Indeed great reading, but not something to do in a noisy bar.

I tried to find some sort of summary on the internet. What I came up with is a foreword by Ray Kurzweil and an editorial review on Amazon.
Both are word reading. The former places Intelligent Universe somewhere along his own thinking on the near singularity, the second one is less positive and points to Gardners limited understanding of evolution, chemistry and physics.

So I will try myself to summarize the book in a two sentences:

The core concept he explains is that our universe is the result of an evolution, optimizing the physical constants exactly this way that live, and consequently intelligent life is possible. Since evolution is something that goes on forever, intelligence keeps increasing as biological intelligence is replaced by technical intelligence, which before the big crunch is able to produce a new baby universe in the next big crunch-big bang transition, with the right fine-tuned physical constants to make life possible etc…

My problem with this is the folowing:
in order to have evolution, you need something that evolves. On earth it is clear that there was an evolution from dead matter to the primeval soup with simple organic molecules to more complex organic molecules to simple living creatures to more complex living creatures to intelligent living creatures to very intelligent living creatures (myself and you, dear reader) and eventually to technical intelligent creatures.
In the explanation of Gardner, I nowhere find anything that comes before a universe fine-tuned enough to permit life. He explains no transition whatsoever between a universe with the wrong physical constants and ours, with the correct-tuned physical constants.
Since it is this live and the consequent intelligence that permits the procreation of this favorable universe, how could a little-bit-wrong-tuned universe, lacking life been selected over a terribly-wrong-tuned universe, also lacking life ?
Evolution does not work with all-or-nothing situations. Evolution needs gradual transitions.

So my conclusion : the book is a nice summary and discussion of interesting literature and speculations, even the great idea (“the biggest of themes”) is interesting, but simply not possible as a result of evolution.

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There is evolution going on in populations of ads in cyberspace !
In the online world of fast media the search agency lets evolve huge populations of ads in stead of one single ad.

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Image via Wikipedia Contact between nerve cells is also constantly being set up and dismantled in adults.
This means our brain is more active and intelligent than we thought.

I wonder if this thechnique could been used in artificial neural networks (ANN’s) ?
This would mean ANN’s where not only the weights between the connections are adapted, but where connections are established and broken during the learning process.

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• Brain Neurons: How They Work

I found these 10 steps in HR in practice: Wiki while you work

Guide to creating a wiki at work in 10 steps

1 Involve employees at the development stage and get their buy-in for the new wiki.

2 Encourage employees to use the wiki to share social as well as corporate information during the working day. Often the social use can drive the corporate use.

3 Make sure your wiki can be expanded and added to over time – it should scale up and evolve with your business.

4 Encourage your employees to create their own profiles for the wiki – containing both professional and light-hearted personal information. Maintain an element of fun.

5 Content for the wiki should embrace and reflect the culture of your organisation and be represented by the ‘tone’ used in the subject matter.

6 Draw your employees into using the wiki by posting daily information that’s unique.

7 Posting company memos onto the wiki will also encourage employees to log into it daily and drive use.

8 Keep training to a minimum to encourage innovation and self-development.

9 Promote the integration of different offices over multiple sites – both professionally and socially.

10 Create a culture of knowledge exchange.

I find these steps great when you want the wiki only to be a information storage. But a wiki can and should be more. A wiki should be a means of collaboration.

So I’ll add following points :

11 (as adults allways remain children and want to follow good examples) : the boss must give the example and have his own personal page (up to date) in the wiki. Better : he should post on his blog at least twice a week.

12 Start with a simple process but do it “wiki only”. Example: agenda of the weekly department management meeting visible for everyone plus minutes of the meeting afterwards (or even better : during the meeting!)

13 Give a training “how to avoid e-mail”

I’m sure there is a lot more. But enough for now.

Image via Wikipedia Some years ago, in a discussion with one of my colleagues, we mentioned the number PI. Sure, you know this is a mathematical constant which represents the ratio of any circle’s circumference to its diameter in Euclidean geometry.
My colleague said : ” For a circle, PI equals 3.1459 “.
“I know”, I said, “but … for a circle ??? Is PI not allways ‘for a circle‘ ? Is there a PI for other geometrical shapes ?”
So we started to discuss the subject and, since a circle is the limit of a polygon with the number of angles approaching infinity, we decided that it must be possible to calculate some sort of PI for non-circular polygons.

What follows is the result. I leave it to the reader to judge the mathematical meaning or relevance, but at the time, I found it interesting and fun.

Let us start with the simple definition : PI equals the circumference divided by two times the radius of the circle. How could we calculate that for polygons ? I had to make a choice : either drawing the circle outside the polygon, touching all its corners, or inside the polygon, touching its sides. I chose for the former one, because you can still draw that circle for a polygon with two angles (a line !).

And then I started to calculate, and frankly, do not ask how I got to it, but recently I discovered the spreadsheet I made at that time and from it I can see I came up with the following formula :

PI_n = n times (cosinus(180 minus 360/n))/2

That gives values for PI_n for polygones :
with two angles : 2
with three angles : 2.598
with four angles : 2.828
with five angles : 2.939
with six angles : 3.000
with ten angles : 3.090
with twenty angles : 3.129
wiht thirty angles : 3.136
with fourty angles : 3.138
with 100 angles : 3.141
with 500 angles : 3.14157
with 1,000 angles : 3.141587
with 10,000 angles : 3.14159260
with 100,000 angles : 3.14159265
with 1,000,000 angles : 3.14159 26536 3325

As PI equals 3.14159 26535 89793 23846 26433 83279 50288 41971 69399 37510 and something more, according to wikipedia, you can see that my formula exaggerates it a little bit : my tenth digit after the decimal point is 6 in stead of 5.
But for the rest it looks like a good approach.
And the beauty of it is that it works also with decimals !
So you can calculate PI for a polygon with for example 7.359 angles !

Here a plot of PI_n against the number of angles of the polygon :

Image via WikipediaNothing so annoying as reading a report full of three- and four letter abbreviations, half of wich you do not understand.
I make the habit of sending an email to the writer to ask for the translations. Why do they not follow the simple rule of writing the expression in full the first time, followed by the abbreviation in brackets ?

The same with jargon. I suppose some people think : why make it easy when we can use difficult or fancy words ?
So I fully agree with this post of Scott Berkun : Why Jargon Feeds on Lazy Minds
I quote : “… all you need to remember is this: never use a fancy word when a simple one will do. If your idea is good, no hype is necessary. Explain it clearly and people will get it, if there truly is something notable to get. If your idea is bad: keep working before you share it with others. And if you don’t have time for that, you might as well be honest. Because when you throw jargon around, most of us know you’re probably lying about something anyway….”

If knowledge management means among others smoothly transferring information, then we must remove anything that makes it more difficult to understand. You know, communication is not about sending information, but getting that message received and understood !

Image via WikipediaSwarms are more intelligent than the individuals of the swarm. How could for example termites otherwise build their sophisticated termite hills ? Or how could ants show such complex behaviour ?
A good article to learn more about it is this from Eric Bonabeau, Marco Dorigo and Guy Theraulaz or this presentation from Thiemo Krink.

This intelligence is the result of information that is transferred to the other members of the swarm. This information transfer can be done two different ways :

1. stigmergy : indirect communication based on modification of the environment. For example ants, who deposit a trail of pheromones on their way, to lead the other ants to the food source
2. direct communication : honeybees who exhibit a particular dance to show the direction and distance of the food source to the other bees.

In artificial swarm intelligence several algorithms have been developed to copy this swarm intelligence mechanisms for data mining purposes.

Here I want to go a little further than just ants and honeybees and see if other swarm animals can offer some ways to develop swarm intelligence algorithms.

I already mentioned ants and honeybees.
In a post a while ago I also wrote that one of the algorithms used in the experimental software Antminer+ more mimics the behaviour of locusts than that of ants.
If we zoom in on locusts, how could these animals transfer their information to other locusts ? By stigmergy. As ants only deposit pheromone to indicate the path, artificial locusts (not real ones) have to do better than that : as locusts jump from one place to the other, the previous locust has to indicate the direction where to jump and eventually the distance of the jump, in order to get to a good spot. This complicates the life of the locusts a bit, because they have to come back each time to leave their information in the previous spot. But it could work.
So you can see that locusts and honeybees sort of communicate the address of the good spot to their swarm fellows.
There is yet another animal species that does this in a even more accurate way : homo sapiens. We, humans.
So why not build a swarm intelligence algorithm where people come back from, say a shopping trip, and write down the address of the store where it is cheap, or good quality, or whatever is favorable.
As in any swarm intelligence algorithm there is some incertainty about all this information transfer, so that the others arrive approximately on the spot, but eventually also look in the neighbourhood.

And just another thought : why in stead of only communicating info of the favorable spots, should we not also communicate info of the particularly bad spots, in order to get others to avoid those spots and do not waste any time there when they do some random searching?

With the microsoft messenger (MM) data, Eric Horvitz and Jure Leskovec at microsoft calculated that each pair of persons using MM is on the average 6.6 degrees separated.
They calculated this from the data of 180 million MM users. This means that roughly each MM user sends messages to on the average a minimum of 19 different persons. That is when all those persons are different. Since there is a huge overlap between the people I know and the people that I know know the real number will be a lot higher.
This conclusion comes from a very simple spreadsheet of which I show the resulting chart here.

number of people connected / in network

.

You see that the vertical axis is in logarithmic scale.
The two red lines cross somewhere between the 6 and 7 line (to approximate the 6.6 degrees) and on the 180 million people. That gives a figure of 18 on the horizontal axis.

I know this is “spielerei”, but the result seems logical.
I wonder if the people of LinkedIn, Facebook, SecondLife etc… will do the same, just to check the number ?

Image via WikipediaAbout a quarter of a century ago, I heard someone giving a talk about “megabases” : databases so large that the daily maintenance window was getting so large that it exceeded 24 hours…

Nowadays people are talking not about doing the maintenance but about mining the information that is stored in databases of sizes like terabytes and terabytes.

You can imagine that this is not something you do in 15 minutes on your laptop.
An especially good post with practical advice on this is
going viral without going down from Saran Chari.

Image via WikipediaThis list of John Elder, Ph.D is not so recent any more, but is worth looking at every once in a while in order not to forget them :

1. Lack Data
2. Focus on Training
3. Rely on One Technique
4. Ask the Wrong Question
5. Listenen (only) to the Data
6. Accept Leaks from the Future
7. Discount Pesky Cases
8. Extrapolate
9. Answer Every Inquiry
10. Sample Casually
11. Believe the Best Model.

(the counting error is John Elders’, not mine!)

I destilled also a more recenter list from this splendid paper of Doug Wielenga on the 2007 SAS forum :

1. failing to consider enough variables
2. incorrectly preparing or failing to prepare categorical predictors
3. incorrectly preparing or failing to prepare continuous predictors
4. ignoring or misusing time-dependent information
5. inappropriate metadata
6. inadequate or excessive input data
7. inappropriate or missing target profile for categorical target
8. target variable event levels occuring in different proportions
9. differences in misclassification costs
10. misunderstanding the roles of the partitioned data sets
11. failing to consider changing the default partition
12. failing to evaluate the variables before selection
13. using only one selection method
14. misunderstanding or ignoring variable selection options
15. choosing settings in the chi-squared or R-squared mode
16. failing to evaluate imputation method
17. overlooking missing value indicators
18. overusing stepwise regression
19. inaccurately interpreting the results
20. ignoring tree instability
21. ignoring tree limitations
22. failing to do variable selection
23. failing to consider neural networks
24. misinterpreting lift
25. chosing the wrong assessment statistic
26. generating inefficient score code
27. ignoring the model performance
28. building one cluster solution
29. including (many) categorical variables
30. failing to sort the (assessment) data set
31. failing to manage the number of outcomes

Two totally different lists. Has data mining changed that much ? No, people have learnt in the meantime and also : the first one is more general : do not misuse data-mining, the second one is more technical : when you do it, do it good !
Both papers are worth reading ! Enjoy!

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What do these two have in common ? Data mining is a serious job, while sex is… well … fun.

(First there was the quote ” data mining is like sex for 10-year olds : they all talk about it, but no one does is”. But that time is past : not that 10-year olds have sex, no : everyone does data mining !)

To start : data mining is FUN ! Any professional data miner who thinks otherwise should find another job (selling fish or so…).

All data miners know that data mining is 95% transpiration and 5% inspiration : this means : work.
You have to do all the data extraction, cleansing, etc. etc. :
In sex : preparing the battleground : making an appointment, have a dinner together, go shopping to buy her (him) a present …

Then comes the more enjoyable part : doing the modeling : sampling, feature selection, trying different
models, evaluation of the models. If you neglect this part, the model will be worth nothing.
In sex : foreplay is extremely important, if you neglect it …

At last comes the climax : you evaluate you final ensemble model against a fresh file of unseen data and get all you did it for : a good, reliable, robust, useful model. You look at the results for a while and see that it is good.
In sex : well, you get all you did it for …, and together you look at eachother and see that life is good.

Then you tell it to your boss, write a report, give a presentation and you are proud to see all those looks full of admiration.
In sex : you talk to your friends and tell them what a wonderful night you had and you enjoy how they envy you.

And then up to the next model (both in data mining or sex)

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Image via WikipediaA machine that can reproduce itself ?
Apparently there is a working prototype.
Well, not fully working. It creates his own parts which have to be assembled by a human. And from the picture you can make the deduction that it did not reproduced the parts and memory contents of the computer that takes part in the process.
But nevertheless it is a beginning of a journey in the direction of a world populated by machines that do not need humans any more to reproduce themselves. And where self-reproduction exists enters evolution. The matrix, but then the real one.
Will we see it (not if but) when it happens ?

A recent article on Kurzweilai talked about harvesting energy from space.

“A single kilometer-wide band of geosynchronous Earth orbit experiences enough solar flux in one year to nearly equal the amount of energy contained within all known recoverable conventional oil reserves on Earth today,” the report said. ”

Yes, and enough energy to fry the earth ! Did they ever hear of global warming ?

Image via WikipediaWho is in charge ? The boss ?
Until some decades ago a farmer was totally in charge of his farm. He knew how and wen to work the land, how to harvest, when to put the bull and the cow together etc. He knew all the details.
The modern farmer nowadays has to rely on computer data to know when to inject what in which animal, on soil analysis data to know what and how much fertilizer to use. His is no longer in control. Because of the optimisation of the way of doing agriculture and the accompanying complexity he has to delegate a lot of tasks to others, to computers.
And here I am still talking of a one man agricultural company.

What does a CEO know about his 5,000 employee enterprise ? How much of the details does he know ?
Almost everything is delegated to his employees in an official hiërarchical structure and an unofficial but vital I-know-who-can-solve-this-problem cooperation network.
The result is that a lot of CEO’s get fired (they do not call it that way and they get huge ‘goodbye’ bonuses but the result is the same) becaus they cannot handle the complexity any more. Even with all the delegation down to all the hierarchical layers, he is bound to fail if he does not know enough of the structure and their interdependencies to be able to predict the consequences of his decisions. As the enterprises become still bigger, and altough things like knowledge management and enterprise 2.0 are getting more and more attention, the latter seems more and more impossible.

How did evolution solve this problem? There is not much hiërarchy in biology. You have the leader of the herd and that’s about it. Or not ?
It seems that there is a whole lot of hierarchy going on in our brains. When we want to walk, we do it just like that. No need to decide which muscle has to contract first, second, with what strength and speed. How does our brain accomplish this ? Apparently this complex hierarchy is made possible by a complex network of neurones that is sufficiently flexible to adapt and learn without collapsing.

In “Why the Demise of Civilisation May be Inevitable” (and see discussions of it by Al Fin and Detainees) scientists see the hierarchical complexity of our civilisation as the reasons of possible breakdowns of -little- parts of our civilization and the increasing complexity of our networks as the reason that this breakdowns will cause chain reactions of breakdowns up to the destruction of the way humanity now exists.
They can be right, they can be wrong (read the discussion) but why is the network + hierarchy combination in our brain so robust (I know people who lost parts of their brain and stil behave more or less normally) and why is the network + hierarchy combination in human societies so vulnerable ?

The difference between the two lies in the structure of the network. Human-made networks are … well, made by humans. This means that humans made networks with a much too rigid structure. They contain too much strict rules. It makes me think of the spaghetti programming style that was used in the 70’s and 80’s of the previous century. If you “pulled” on one string of the spaghetti, all the rest was affected. There were programs no one even dared to touch because you could not tell what the effect on all the rest was going to be.
Our brain networks are not designed, they are grown, just like everything in biology, just like ecological networks. It took evolution thousends of centuries to get to it.

Humans do not have that time, so we design everything to work optimally, but only under the given conditions. But conditions change from time to time.

The biological solution is not the optimal solution, but the one that is ‘good enough’ even in changing circumstances. That is “Why our brains are so clumpsy“.
That is also why data miners nowadays “grow” models in a similar way. In stead of using one statistical equation they use artificial neural networks, ensembles of weak learners, to grow some sort of holographic models : black box models where the rules are distributed troughout the whole model but which are very robust. For this robustness is exactly what data miners seek : form a given set of data (=specific conditions) make a model that can pinpoint the as correct as possible outcome for other (altered conditions) data.

Our financial networks need a similar solution to become robust enough to withstand events as a simple credit crisis.
The problem with our economical and financial networks is that “as correct as possible” is not good enough. The balances on our accounts have to be correct up to the penny. As long as no one will accept a payment of approximately 1,000 dollars we will live with the sword of Damocles above our head : will the system hold or crash ?

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In actkm I found a list of references to KM failure stories
Here I only list the failure reasons to make it easy for you. For more details you should read the stories

• overreliance on a database for problem solving
• to replicate the same knowledge-management system across different departments
• the original team of contributors in a project ends up squeezing out any knowledge from outside the core group
• the Field of Dreams trap: “Don’t assume that if you build it, they will come.” There was no incentive for anyone to invest time and energy to solve other people’s problems
• no process to monitor the quality of the written contributions
• expecting new technology and reengineering of processes to produce a collaborative, sharing culture, where the company’s greatest need was not new technology but a culture modification program to prepare for a KM initiative
• Management says they want it, but everything they do is opposed to it
• belief that professional standing depends on what you know that others don’t
• Technological incompatibility : each file had to be translated to a spreadsheet before transmission
• A respected head of KM at a large multinational consulting firm, who had her budget cut to nothing by senior management
• Defining knowledge within functions or silo-oriented communities of practice does not work. Instead define knowledge at the level of business processes.
• forgetting that a knowledge management initiative must relate knowledge to people’s day jobs.
• Attempting to apply Information Technology to tacit knowledge. This is fraught with difficulty. Instead, it is explicit knowledge that is most susceptible to the application of Information Technology.
• Failure to carefully manage external input to knowledge management initiatives managed to ensure people within the organization are in control of the initiative at all times.
• Failure to understand the organization’s willingness to change and to manage people’s expectations appropriately.

Did you enjoy this post ? Then you might be interested in the following :
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list of Knowledge Management-lists
List of Knowledge-management lists : V2
Knowledge management = Change management
15 ways to use knowledge management software

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• Oh when will they ever learn?

                                     

Image via WikipediaA while ago I wrote a post about dragons. It was more or less meant as a joke, though it ended up as my most popular post !
Apparently a lot op people want to know if dragons really existed.

The facts that

1. there is a lot written and told about dragons throughout history
2. people are still interested in the subject and it takes part in many cultures
3. apparently there is no scientific proof of their existence

makes me think of a few other concepts with similar characteristics : Ghosts/spirits, Gods & angels, Devils.

Now all the dragon stories must have come from somewhere ! Either there was something out there that made people talk about and make stories of, or it was just something out of our imagination.

For the first possibility let us ask three questions :

1. did dragons exist ?
2. did dragon-like animals exist ?
3. did dragons or dragon-like animals co-exist with modern humans ?

As to the first question : I already said that there is no scientific proof that they existed.
For the second question : there is ample proof for the existence of dragon-like animals : a lot of our dinosaurs are ugly enough to be considered as dragon-like : triceratops, T-rex, etc…

The third question is also easily answered : even still now we have a lot of very ugly and scary animals : crocodiles, some really ugly fish, and obviously the komodo dragon.
Are these ugly and scary enough to lay at the origin of the dragon stories ?
The Genesis park does not think so and considers the dragon myths “…the evidence that dinosaurs and man were created together and have co-existed throughout history…” Although they present interesting material we all know that there is no sufficiently recent remain of a dinosaur for their theory to be true.

A cute alternative is that people saw comets as dragons, like a lot of people interpret natural phenomana as flying saucers.

My personal feeling is that human imagination and creativity is sufficient to put some things together in order to create dragons, gods, devils, ghosts and the like. People like to exagerate and since it is easier to speak about black and white than about all colours of grey they created extremely good, extremely bad, extremely ugly, extremely dangerous etc. imaginary creatures, dragons being the combination of extremely bad, extremely dangerous and extremely ugly. Note that chinese dragons are more benvolent

So what are the ingredients :

• Dangerous (fire, large predators, enemy tribes) : it can kill you, burn you and eat you, so it needs a big fire and over-developed fangs and claws. Also it posesses the fastes way of transportation, so that you cannot outrun it : it flies (altough the chinese ones do not). Oh yes, and it is huge !
• bad (murder, rape, robbery) : it kills for pleasure, steals your virgins and treasures, burns your towns etc… and it has to have a sort of human mind (it thinks and speaks).
• ugly (old or sick people or animals) : “accidented” cold, bony skin, and no soft feathered wings, but ugly bat wings ( a bat is a night animal, and night equals danger).

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Related articles

• Dragonology The Movie
• Western and oriental dragon

It has been a while I wrote about sharing my personal woas at work with my two colleagues. They were enthousiastic, but ever since they did nothing with it, unless sporadiccaly looking up some information.
So I consider this more or less a failure.
It has nothing to do with the “personal” of the personal wiki. There was nothing personal in it. It is just that they are not used to wiki’s.

Me, I keep it up to date.
I also started a second one at home for whatever info. Very useful.

By the way : at work we use MS Internet explorer, at home I use Mozilla Firefox. With internet explorer it looks a bit ugly, with firefox it is perfect.

Image via WikipediaGeorge Musser wrote “The Complete Idiot’s Guide to String Theory“.

Talking about extremes. Is it possible to explain one of the most difficult theories to dummy’s ?
I wonder.
Here you can see more.

Perhaps I will once read this book. Did anyone ? Was it good ?

Image via WikipediaSometimes I see that people arrive on my blog by typing the above question in a search engine.
Are men and women different species ?

First of all : what is a species? This is not so simple. According to wikipedia, a species is “a group of organisms capable of interbreeding and producing fertile offspring“. It is clear that this definition only holds for organisms which reproduce sexually. For the others, there are apparently other words.
Moreover, in an interesting paper, Ernst Mayr points out that a species is not just a man-made unit of classification of animals, but a concrete phenomenon of nature : a species is “… principal unit of evolution and it is impossible to write about evolution, and indeed about almost any aspect of the philosophy of biology, without having a sound understanding of the meaning of biological species“.

The fact that men and women can have fertile children make them one and the same species. No doubt about it.

And what about all the differences between them ? This is nothing special in the animal world. Look at the birds : a lot of species have dull-coloured females and brightly coloured males.
The praying mantid female is much bigger than the male, she even bites his head off right before copulation !

But what if with all the technology, women will not require a man any more to have children ? No big deal. It is not because they do not interbreed that they can not ! If John has no relation whatsoever with Mary and consequently they do not have any children together, does that mean that they are different species? No, it is not that because they do not that they can not !

Did you enjoy this post ? Then you might be interested by the following :
Human procreation strategies
are men and women different ?
Imbalance of cheating

                                  

Image via WikipediaA lot of people wear glasses to improve their vision, use a cane to walk better, have dental implants. Is this a beginning of human cyborgs like robocop : a mixture of nature and artificial ?

Let us go further and see what is already possible :

• knee replacement
• hip replacement
• artificial limbs
• artificial organs
• breast implants like Pamela Anderson
• an artificial penis

That is a whole lot of artificial stuff to put in a human.

But there is more :

• neural implants to move arm
• retina microchip
• a monkey feeds itself using its brain, yes, reading our brain(1) (2)(3)
• controlling our brain : implants to cure depression
• virtually walking
• and to cure cancer and the like :a chemical brain to control nanobots
• and inevitably al lot of ways to build a better soldier

It seems we are well on our way to become cyborgs !

Enjoyed this post ? Then you might be interested in the following :
– Web 5.0: The telepathic web
– Futurology : Top ten emerging technologies
– Robotic insects or cyber-insects ?
– Self reassembling Robot
– Human brain copy protection by AnyMind Inc.