Image via Wikipedia

Ben Goertzel tweeted the following 3 tweets today :

• Option A: you are tortured (with no permanent damage) and then the memory of the torture is erased.
• Option B: you are not tortured and then a false memory of torture is programmed into your brain.
• Which do you choose, A or B?

No funny thoughts, rather one of those choices you really prefer to never have to make. But if YOU had to chose, which one would it be ? A or B ? Let me know please !

His first 9 responses were : A : 7, B : 3
My own response : B (no actual pain) but afterwards I would go to let myself hypnothize to remove the awful memories !

Makes me wonder : After the facts : what is real ? The memories you have seem to be real, but if there is a way to put memories there without having experienced the real situation, for you there memories correspond to the real situation.
I am sure there are ways to put memories in someone’s head ! A tough interrogation may result in the subject actually believing he was there, he saw this or that or he actually did it ! (see these articles : (1) (2) (3)

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• Torture Interferes With Memory (scientificamerican.com)

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did dragons exist (II)

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the origin of new genes
The Number of Parallel Universes
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is the Higgs boson sabotaging its own discovery ?
if Matrix was programmed on windows XP
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Enjoyed this post ? Then you might be interested in the following :
link list for september 2009
link list for august 2009
link list for juny 2009
link list for may 2009

Where is your soul located ?

(my working synonyms of soul : self, consciousness, spirit, identity).

First, and obvious answer : in your head.
According to Douglas Hofstadter in “I’m a strange loop” this is not entirely true.

Explanation :
1. what is my soul : a whole bunch of patterns in my brain (linked, hierarchical “thoughts”, patterns representing concepts). One of these patterns is special, because it groups everything that relates to “me”.
2. not every brain pattern that relates to me is in my own head. A whole lot is in the heads of my friends, my family. Although not so vast as the one in my own head.
If the sum of everything that relates to me is my soul, then I am distributed over the heads of a lot of people.

Does this sound a bit crazy ?
After all, I only have one mind, and everything about me that is in the mind of somebody else is not “me” but is what that other person thinks and knows about me.
So that is what I thought before I read the book.

But let us do a hypothetical experiment. (Douglas Hofstadter describes some experiments like that in his book, but this one here is my own).

Imagine one brain to start with, with twice the number of neurons of a normal brain.
Imagine we can manipulate physically each neuron as we like.
Imagine we take at random every second neuron and put it in a second (empty) head. When we finish, half of the neurons will be where they originally were, namely in the first head. The other half will be in the second head.
Imagine we left the original neuron-neuron connections intact, meaning that we replaced every “broken” connection by an artificial equivalent wireless connection.

The result :
fysically (or rather “locally”) we have two brains, each in it’s own head. Let us call them Adam and Eve.
functionally, they are still the same original superbrain because all neurons and connections are unchanged. In fact, we now have one brain with two bodies. What would this be like ? I assume that brain will control the two bodies, just like you and me control our two hands. Consequently there will be only one “me” (named AdamEve).

Now assume that some of the wireless connections are broken, or of lousy capacity, so that only part of the info is passed on from Adam’s neurons to Eve’s neurons and vice versa.
This means that all thoughts, concepts etc. formed by only Adam’s neurons will be stronger, and clearer in Adam’s head thatn in Eve’s and vice versa.
Result : the “shared” identity AdamEve will be weaker. In the same time two separate identities will probably develop : Adam and Eve.

Now suppose all wireless connections are replaced by words, sounds, expressions, gestures, emails, writings or whatever people in our real world use to communicate.
Result : the shared identity is very, very weak whereas the separated identities are very strong.
We all know such shared identities : a married couple, a football team, an army, a religion …

But this is not what Hofstadter writes ! In stead of talking about shared identities, he speaks of pieces of identities that are scattered over the minds of many people. Or if we only consider two people : the two separated identities live in the two heads.

Let us recapitulate :
if there is no connection there are two completely separated identities.
If the “between-people” connection is equally strong as the “intra-people” connection (as in my split-brain thought experiment), according to Hofstadter we have two separate identities, living equally strong in both heads. According to me, we have only one shared identity and no separate identities.
If the “between-people” connection is weaker than the “intra-people” connection, according to Hofstadter we have two separated identities each living in two heads, but the one living in its own head is stronger than the one living in the others head. According to me we have two separated identities plus one, weaker, shared identity.

Enjoyed this post ? Then you might be interested in the following :
- Web 5.0: The telepathic web
- Robotic insects or cyber-insects ?
Psychons : elementary particles of the mind
- Human brain copy protection by AnyMind Inc.
- Humans 2.0

Data mining and other forms of analytics have one primary goal : making invisible paterns visible. The information is in the data, but it is invisible for you if you are not a “Homo analyticus”.
Some examples :

• the most significant changes in employee’s behavior (a “hidden” pattern change) occurred one month prior to the collapse of the Enron company
• creditcard fraud detection by data mining
• unlocking patterns and structures in huge volumes of texts
• finding patterns in the phone records of 9/11 terrorists

If this seems somewhat mysterious for you there is a simple way to make this all visible. Although it is not really data mining, it is a somewhat funny way of showing what it means to see “hidden patterns made visible”

Who said there that humans stopped evolving because their technical means took over the necessity ?

blue eyes

• Steve Jones (here)
• Eric Bruce (here)
• Steven Pinker (here)

Apparently this is not true !

It seems that on the contrary since humans started spreading over the entire world some 50,000 years ago their evolution speed shifted to a higher gear.

Some examples of evolution that took place the last 50,000 years :

• human skull structures of various ethnic groups evolved in different directions (remember we all came out of Africa, looking more or less alike)
• people living in the Tibet mountains have a special gene which causes the oxigen level in the blood to increas with 10%
• Scandinavian people have blue eyes : no blue human eye existed before the last 10,000 years
• sub-saharan Africans developed already 25 genes protecting them against malaria, a disease that is only 35,000 years itself
• a gene that enables men to digest lactose (a milk sugar) is 8,000 years old and only came into existence after people began to keep cows
• and a lot of others

Why this speeding up ?

As I wrote earlier in order to have evolution you need i) diversity, ii)(re)production and iii)selection by the environment.
All three of them are present in our last millenia, so there is no reason why there should not be any evolution of the human race.

But there is something remarkable in our recent history : we came out of Africa and conquered the whole planet which means that :

• number of people was considerably growing , and consequently also our diversity
• we started to live not only in environments very different from the african plains, but also from eachother, from the ice sheet of greenland to the Sahara desert, to our modern Manhattan

So no wonder that this incredible shift in environments and density caused an incredible speeding up of our evolution. At least 7% of our genome has mutated recently (in the last 40,000 years).

Did you enjoy this post ? Then you might be interested in the following :
top-10 lists on evolution
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Human evolution : the future of men
Evolution towards Intelligent Design
The end of evolution

“Telepathy on the Horizon: New Interface Allows Brain-to-Brain Communication”

Is that so ?

I thought not.

First of all, if you did not read about it or saw the video, it is a good time to do it now : (article, video)

What did they do ?

They connected brain A (a person who was thinking ‘lift left arm, lift right arm …’ to represent zero’s and one’s) to an EEG transmitter and then to an PC (pc1). This PC1 was connected via the internet to another PC2 which interpreted the transmitted brain patterns to ‘on’ or ‘off’ signals and used them to flash a light. The second person (brain B) saw the light, was also connected to an EEG transmitter and then to a PC3. This PC3 interpreted the brain B patterns to reproduce the original zero’s and ones.

OK, good technology, but definitely not telepathy.

Because telepathy is “transferring knowledge (understanded information) from one person’s brain to another person’s brain without using the normal means (gestures, speech, writing … to send and our five senses to receive). In the experiment the second person was not even aware of the information. He only saw the light flashing.
In his setup Dr. Christopher James at the University of Southampton has only used one direction of communication : “exporting” a meaningful pattern from the brain. He did this twice, once on both sides of the communication.
These one-directional computer-brain-interfaces are around several years now.

Real telepathy.

For real telepathy you should also be able to do it the other way around : put information back into someone’s brain without using this person’s senses. And that’s the tricky part. I am not aware of any experiment that managed to do such a thing.

Enjoyed this post ? Then you might be interested in the following :
- Web 5.0: The telepathic web
- Robotic insects or cyber-insects ?
- Self reassembling Robot
- Human brain copy protection by AnyMind Inc.
- Humans 2.0

Here is my link list for september 2009.
Enjoy reading (but don’t forget to take a look at my own writings too )

Evolution of Darwin’s “on the origin of species”
Eleven carbon removal projects to stop global warming
Sustainable fertilizer : urine and wood ash, to grow big tomatoes.
Cities are organized like human brains
10 Awesome Websites That Help You Discover the Best Web Apps
the computer/lawyer
Men Losing Their Minds Over Women
Human brain could be replicated in 10 years
Memories exist, even when forgotten
The Hottest Tweets on Twaxed.com
Viral video about social media revolution
did you know 4.0
Wall Street’s Math Wizards Forgot a Few Variables
Scoring with Social Media: 6 Tips for Using Analytics
The most important writing lesson :”Nobody wants to read your shit”
cracking the brain’s numerical code
Schrodinger’s cat experiment proposed
First online under water observatory
Website analysis : internal search site analysis
computer ants to fight computer worms
information is beautiful

… ever modeled the lift of a targeting model ?

In a previous post “Data mining for marketing campagns : interpretation of lift” I discussed the factors that influence de lift of a targeting model. Apart from the quality of the model, the lift is theoretically also influenced by
- the natural return = normal percentage of buyers among your customers during a specific period
- the size of your selection in % of the customer base

As a reaction to my post, Tim Manss, in his post I’ll show you mine if you show me yours… proposed to exchange lift figures in order to be able to have something of a benchmark to check the quality of targeting models.
It is indeed not easy to get these figures, because everybody wants to keep his or her secrets … well, secret.

So I decided to give away at least some info about the lift of my targeting models by calculating a model predicting their lift.

Here is what I did :

I took the lift figures of my models (a handful of dozens of them) together with the natural return and 4 different selection sizes : 10%, 5% 1% and 0.5%
And with this simple dataset I calculated a linear regression (I actually used the logarithms of these data).

What turned out ?

- There was of cause a lot of noise : R-squared = 0.45 which means that more than half of the variance is unexplained noise. Which also means that different targets have different predictability.
- the natural return showed no statistical significant meaning
- so the only relevant predictor is the selection size.

Here is the equation and the corresponding chart (lift=ordinate, selection size=axis)

ln(lift) = 3.06291 – 0.4829 * ln(selection_size)

Lift (vertical) as a function of the selection size (horizontal)

So, I showed mine… what about yours ?

Other posts you might enjoy reading :
data mining with decision trees : what they never tell you
The top-10 data mining mistakes
Good enough / data quality
Data mining for marketing campaigns : interpretation of lift
Are you a good data miner ?

Two interesting articles of Gregory Piatetsky-Shapiro (KDnuggets) on lift modeling :
Measuring lift quality in database marketing
Estimating campaign benefits and modeling lift

Tough question. What is a good data miner ?

One way of finding out is to look at the job descriptions, for example this one : Credit Suisse Data Miner Job Description

M.George distinguished five areas of expertise necessary to be a good data miner :

1. techniques : to be able to do it
2. analytics : to be able to decide what and how to do it
3. business : to understand your customers
4. communication : make your findings clear to others
5. project management : manage everything and everyone from start to end

But all that still stays a bit abstract.
In what follows I will try to be somewhat more to the point.

Let us start with the data.

You have to be a bit of a detective just to find your data. Find the people who know where the data is, Find out how you can access the data. Find out who can give you access rights to the data, Find out the corresponding key variables to join the various tables into one flatfile …

Then you have to be a programmer to put all that info to use : sql, sas, BI tools, R, whatever not only to get your raw data, but also to get them usable : what to do with missing values ? which derived variables wil you calculate ? etc…
A lot of technical skills needed.

But there is not only the data, there is also the problem to solve. So you need to be an analyst.

As an analyst you have to make decisions about doing the things right and doing the right things :

• take a step backwards, know where to start, where to stop
• question everything : allways ask yourself where you are wrong, not good enough, to complicated, not efficient enough, …
• question everything : when they ask for numbers, ask them to explain their problem and how these numbers will solve it. Propose better, cheaper, nicer solutions …

And now comes the fun part : you have to be a number cruncher
You love data, charts, statistics (not the theory, but what you can do with it). You love to explain to people why something happens, to show them relationships between numbers, the conclusions that you derive from your numbers …
You know the data mining techniques, the statistical techniques and what you can and cannot do with them, their advantages and drawbacks, how to interprete the results, how to present the results in an uderstandable way (remember :
the others are stupid and lazy,
so you have to make it simple and easy
).

Unfortunately there is also the business (profits, costs, ROI …)
They expect you to deliver usable results in a short time. An accountant must deliver numbers that are correct, a data miner is lucky : nothing has to be absolutely correct. When it is good enough, deliver ! (Think “Microsoft software quality” !).
They sometimes say : a data mining model is never finished, only the data miner stopped working on it. This is very true, so keep that in mind and know when to stop and deliver !

Of cause every data mining project is, wel … a project. So you have to be a project manager too.
As a project is per definition something with a start and an end, you should have somewhere a description (accepted by all involved parties) of “WHEN CAN YOU CONSIDER THE PROJECT AS FINISHED”. This description is the only thing you need, because it has to contain all the conditions that have to be fulfilled (goals, deliverables, quality metrics …).

What helps you to deliver more quickly is to stick on the following rule : do the same thing twice, but never do them three times. This means that for anything you will have to do more than two times you should find a solution to get it done automatically : write a program, download a program, write an excel macro, anything.
this means you also have to be a bit of a software engineer !
This automatisation/industrialisation holds for anything : data extraction, modelling, model result reporting, monitoring of your model quality, monitoring of the data quality etc …

And last but not least : you have to be a learner.
Never think you know it all, allways look for new ways, read articles, go to symposia, find out how ohters do it, look for ways to deliver as much quantity and quality as possible whithout working too much

Other posts you might enjoy reading :
Oversampling or undersampling ?
data mining with decision trees : what they never tell you
The top-10 data mining mistakes
Good enough / data quality
Data mining for marketing campaigns : interpretation of lift

… or do they talk about another ROC and another LIFT ?

Other posts you might enjoy reading :
Data mining for marketing campaigns : interpretation of lift
Howmany inputs do data miners need ?
Oversampling or undersampling ?
data mining with decision trees : what they never tell you
The top-10 data mining mistakes

Dear reader, enjoy my list of interesting articles I found in july.

Ten Great Ways to Crush Creativity
Top 10 Signs You Might Be a Geek
Social Media’s Top 10 Dirty Little Secrets
Piwik open source web analytics
the power and value of a fan (in social media)
open source text analytics
billion dollar gram
DNA computation gets logical
The Remotest place on earth
How to Ask a (Near) Stranger for a Favor

This is a post about the lift of a data mining model for marketing campaigns. The topics discussed are :

• Definition
• Wait ! Why lift and not AUC ?
• Your selection size determines your lift
• Your target class proportion determines your lift
• How can you use lift for model comparison ?
• Large lifts, still small profits?
• To simplify : user returns in stead of lift

Definition (Wikipedia) : … measure of the performance of a model… The lift of a subset of the population is the ratio of the predicted response rate for that subset to the predicted response rate for the population.

Wait ! Why lift and not AUC ?
The Area Under the ROC-Curve is often cited as a better, geneal measure of the quality of the model. It allows you to compare different models. OK, right, but 1) try to explain AUC to your HiPPO’s. and 2) when you use your model for marketing campaigns you are only interested in the performance of a small selection of your customers, the ones with the best scores.
So : lift is simple, and you can make it even more simple. Read on.

Your selection size determines your lift
If you read the definition, you saw : … “lift of a subset of the population” … Normally you take a subset of the population with the best scores, the ones you would use in a campaign.
The size of your selection determines the upper limit of the lift.

• 100% of the population : lift is per definition = 1, meaning the performance of this selection = the performance of the total population. Of cause it is useless to make a data mining model and then use the entire population.
• 50% of the population : Upper limit = 2
• 25% of the population : Upper limit = 4
• 10% of the population : Upper limit = 10
• 5% of the population : Upper limit = 20
• 1% of the population : upper limit = 100
• etc.

You see it makes no sense to say : “my model has a lift of 10“. This means nothing. It depends for a great deal on the selection size.

Your target class proportion determines your lift

Imagine you want to predict how many people in a selection will buy products A or B during,say, next week. Let’s say that normally you sell 100 pieces of A and 5,000 pieces of B in a week and the two products are equally predictable, meaning that the two data mining models are of comparable quality. So which model will show higher lifts for equal selection sizes ?
Again the proportion of the target class determines the upper limit of the lift :

• if you only have 5,000 customers in your database the lift for product B will be … 1. Since every client buys product B in a week you cannot get it higher with a model.
• if you have 50,000 customers, the highest possible lift will be 10. Why ? The proportion of buyers in the entire population is 10%. If with a very good model you can make a selection of 5000 (or less) where everyone buys than you get 100% buyers in your selection which is 10 times better => hence a lift of 10.
• if you have 5,000,000 customers and your model enables you to make a selection of 5,000 (or less) where everyone buys you compare 100% buyers with the 0.1% buyers in the entire population, which gives you a lift of 1,000 !

How can you use lift for model comparison ?

The way I do this is rather straightforward. I take the lift of all my models for the same selection size and plot the lift against the proportion of the target class.

This gives something like this with lift on the vertical axis and selection size on the horizontal axis :

See that the star is lower than the flower ? Nevertheless the “star” model is of better quality because its lift is one of the best compared with the other models, whereas the “flower” model is relatively poor.

Large lifts, small profits ?
What does a large lift mean for the return of a marketing campaign ? Absolutely nothing !
The return of a marketing campaign depends on (among others) :

• the fixed costs for the campaign (making the model, paying for the administration,
• the variable costs for the campaign (paying for the publicity, costs per letter when using snail mail, ….)
• The number of surplus sales (= the number of sales in the campaign minus the “normal” number of sales : expected number if you did no campaign)
• the gain in $ per surplus sale

So where does the data mining model come in ? The number of surplus sales depends on the impact of the e-mail, letter, phone call on the client behaviour : will he/she buy, whereas without the e-mail he/she would not ? If thanks to the data mining model you selected a very good target group the impact will be bigger.
And now the lift :
Case A : 1) normally you sell a 1,000 pieces to 5% of your customers. 2) You select a target group of 5,000 customers with a sales rate of 10% (=> lift = 2). 3) the e-mail impact doubles the success rate which means that you sell 1,000 pieces to that target group of 5,000 customers. Hence you get 500 surplus sales.
Case B : 1) normally you sell a 20 pieces to 0,1% of your customers. 2) You select a target group of 100 customers with a sales rate of 1% (=> lift = 10). 3) the e-mail impact doubles the success rate which means that you sell 20 pieces to that target group of 100 customers. Hence you get 10 surplus sales. If each sale of product A is worth the same amount of $ as product B it is clear that the high lift in case B is worth much less than the lower lift of product A.
Lift is just … lift. You have to lift something. It means more to life a huge quantity a little bit than a tiny quantity a lot.

So do not waste your time to develop targeting models for products that do not sell !

To simplify : user returns in stead of lift
For some HiPPO’s lift is still something too complicated. In that case use a simple return chart : take the lift chart in this post, but replace the life by the percentage of buyers. It is very easy and it is much more business language. You can directly tell if using the model is worth wile.

%-age of positive targets in relation to the selection size

Notice that the “lift curve” shows how much the %-age of positive targets is “lifted” above the baseline (random selection).

Other posts you might enjoy reading :
Howmany inputs do data miners need ?
Oversampling or undersampling ?
data mining with decision trees : what they never tell you
The top-10 data mining mistakes
Good enough / data quality

Image via Wikipedia

Evolution does not know any direction. Genes that have the highest proportion in the next generation stay in the race, the others gradually disapear. It is the (changing) environment that dictates the direction, not (Darwinian) evolution.

(But see this post : direction is inevitable towards two-legged two-armed humanlikes … : I’m not really a believer in this).

Why Darwinian evolution (variation, selection, reproduction) ? Is there an other form ?

Let’s go back, when there was no life yet. Was there evolution ?
Assume you think :”no, there was no evolution”. So when did evolution start ? The very moment that the first living thing came into existence ?
Wait a minut ! Some dead thing evolved to become that first living thing ? Was that not evolution yet ?
I agree to say that it was not Darwinian evolution : reproduction lacked.
But still : it was some sort of evolution : variation, selection, and production. A bit like the evolution of our cars nowadays. They do not reproduce, they are produced, there is variation, and they are selected by the consumers.

But before life : evolution was all very slow.
To speed things up reproduction was … evolved, “invented” by evolution. It was a paradigm shift. In stead of being produced by chance, the entities were constructed that way that, in the right environment, they were copied. Imagine the advantage of that speed gain (speed = number of copies produced per time unit). So Darwinian evolution was selected as an advantageous strategy. Numerous enhancements evolved like proper genes, cell structures, entire organisms, you all know that.

But after a while, another strategy evolved : in stead to rely on genes to carry all necessary information from one generation to the next, organisms evolved that passed on information directly to each other and to their offspring in the form of memes : culture, communication, education, whatever you call it. Numerous enhancements evolved, like writing, telephone, blogging, twitter, you all know that.
It’s where we stand nowadays.

But after a while, another strategy will evolve. As making predictions is difficult, especially when it’s about the future, the only thing I can write about it is a guess : in stead to rely on biological organisms to carry all necessary info from one generation to the next, artificial (non-biological organisms will evolve and test new information in some artificial intelligent programs, models, whatever and select them before they incorporate them into the next generation. Total automatic scientists already exists (their inventors called them Adam and Eve). So the next paradigm shift will be something like evolution without biology.

Did you enjoy this post ? Then you might be interested in the following :
Human evolution : the future of men
top-10 lists on evolution
Evolution of minerals
Evolution in blue and red
The end of evolution

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• Real-Life Decepticons: Robots Learn to Cheat (wired.com)

Image via Wikipedia

I am sure you all know the three laws of robotics, invented by Isaac Asimov :
* A robot may not injure a human being, or through inaction, allow a human being to come to harm.
* A robot must obey orders given to it by human beings, except where such orders would conflict with the First Law.
* A robot must protect its own existence as long as such protection does not conflict with the First or Second Law.

Are these the only necessary laws ? Are these three (good) enough ? Are there alternatives ?

Recently David Woods and Robin Murphy made up three new laws (Want responsible robotics? Start with responsible humans and The 3 Laws of Robotics – Modified) :

* A human may not deploy a robot without the human-robot work system meeting the highest legal and professional standards of safety and ethics.
* A robot must respond to humans as appropriate for their roles.
* A robot must be endowed with sufficient situated autonomy to protect its own existence as long as such protection provides smooth transfer of control which does not conflict with the First and Second Laws.

Two differences with the “old” ones :
1) the first law is for humans
2) the other law are less specific and hence applicable in more situations.

But Woods and Murphy are not the only ones to rework or discuss or suggest alternatives for the three original laws. Some examples :

Asimov himself : The zeroth law : A robot may not harm humanity, or, by inaction, allow humanity to come to harm

Asimov’s Laws of Robotics
Implications for Information Technology

HOW will a robot identify a human being?

3 laws unsafe
Asimov’s Laws of Robotics Are Total BS

ten ethical laws of robotics

open the future : five laws of roboticists

New Laws of Robotics proposed for US kill-bots

30 laws (not to taken seriously ) :

And even a contest : Winner of the “Maker’s – Three laws of robotics contest

Humans, robots or Cyborgs ?

The most interesting issue here is the clear distinction Asimov makes between humans and robots. In his books, humans were pure humans, robots were 100% robots.
But our future world is not the world of Asimov. Nowadays many people are not 100% human any more : artificial hips, knees, teeth, eyes, hearth valves, pacemakers and whatever are more and more part of our humanity.

On the other hand, experimental robotics does not limit itself to metals or plastic. A recent experiment involved a living rat brain steering a sort of simple robot.

If we continue that way the differences between humans and robots will dissapear. But not only between humans and robots, but also between humans and some animals with enhanced brains (= implants will make them as smart as most humans).

So what about the laws of robotics ?
IMHO lawmakers will have to make laws for everyone, not making any difference of species, technical specifications or whatever you can disciminate between living beings.

Enjoyed this post ? Then you might be interested in the following :
- Web 5.0: The telepathic web
- Robotic insects or cyber-insects ?
- Self reassembling Robot
- Human brain copy protection by AnyMind Inc.
- Humans 2.0

Related articles by Zemanta

• Nine Words You Might Think Came from Science but Which Are Really from Science Fiction (3quarksdaily.com)
• Humanity 2.0 (mobhappy.com)

Image via Wikipedia

How fast can evolution take place?
We usually think of evolution of some process that takes thousands or millions of years.
WRONG !

OK, the really big changes need some time to show up. To evolve from a dinosaurus to a bird cannot happen overnight.

But it does not take centuries to see evolution. This studies on guppies show that evolution can go a lot faster.

It all depends on your definition of evolution. For example R.P.Worden uses in a somewhat theoritical article the “rate of increase of Genetic Information in the Phenotype” to measure evolution speed.
I find this a bit silly. If the genetic information does not increase, but nevertheless changes is that not evolution too ? As if you say that two different books of 350 pages each are the same book because of the same number of pages !

I think evolution works on a much smaller scale : whenever the genetic content of a population has changed between two generations there has been evolution !
This simply means that evolution takes place constantly, because there are always tiny changes between following generations.

In the guppy article, the experiment consisted in putting guppies in two different environments. Everyone familiar with evolution knows that evolution is caused by changes in the environment.

Important changes cause fast evolution, like the guppy experiment, where they saw significant evolution in 10 years.

But it can go even more rapidly : during a cold winter on the average the smallest birds in a population suffer most because their lose more heat than the bigger ones (the Bergmann rule). This means that after an extremely cold winter the average size of a bird population has increased, because the big ones survived better. Since size is inherited it is obvious that the genetic content of the population has changed. IN ONLY ONE WINTER !

Did you enjoy this post ? Then you might be interested in the following :
Human evolution : the future of men
top-10 lists on evolution
Evolution of minerals
Evolution in blue and red
The end of evolution

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• City birds lazier than their country cousins (telegraph.co.uk)

Dear reader, here is my list of interesting articles I found in july. Enjoy reading.

Twitter Better: 20 Ways to Filter Your Tweets

Britney Spears’ Guide to Semiconductor Physics

Bare essentials of safety from Air New Zealand

8 levels of analytics

The generation M manifesto

Born to cheat

Using bacteria as a computer

artificial brain 10 years away

When I saw the site of the Center for the Mathematics of Information, I started to have a somewhat funny feeling : mathematics of information. Is that the same mathematics we use for calculations on for example money-related topics ? I doubt it, because In the past I heard things like :

• if you share information with someone it doubles, because now both of you posess the information.
  Very unlike sharing money!
• Drowning in information : if you have more than you can handle, and you add still more information, you end up with less. Is also called “information overload“.
  With money it is simple : adding money makes you richer, even if you are Bill Gates.
• - Does negative information exists ? : So that when you received it, you know less than before.
  (here I do not mean information about non-pleasant situations or the like) I think it is possible : Suppose you are convinced about something (your info is that X is true). Than you receive new info and as a result you are not sure any more, maybe it is not true after all.
  But there is a much more serious explanation on negative information : according to physicists Quantum information really can be negative.

Did you liked this post ? Then you might be interested in the following :
Information overload, filters and Web 3.
Howmany inputs do data miners need ?
Simplexity : new word about old situations
The family of PI
Is Google God ?

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A study of Duncan Irschick at the university of Massachusetts drew my attention. It says :

Men Are More Accurate than Women When Hitting a Target with Force in the Dark
The story in itself is interesting, but what particularly struck me was that It was quote ” … a small study …” end quote.
I totally agree with that since they “…tested four male and three female adults”.

Yes, right : 4 men and 3 women.

The first reflection of somebody with a statistical/data mining background is : “How on earth can a self-respecting scientist publish results on differences between men and women with such a small sample ?”. I not only mean self-respecting, since he is also respected by oythers and a first-class scientist.

So there must be something else. Could it be that with such a small sample you can indeed do some thorough statistics ?

Let us try it out.

The case at hand is men and women hitting at something with a hammer. I do not know the details, but for the present purpose it is simple to use some fake data.
Let us take an extreme case :
If they must hit some target, suppose that the four men missed the target by 20, 18,22,and 21 centimeters respectively. The women, being much accurate missed only by 3,5 and 6 centimeters.
With a simple t-test we find out that the two means of 20.25 for the men and 4.67 for the women are significantly different (p=030003; two-tailed).
So if all 3 women are far better than all 4 men we have a proven case !

With one woman being a bit less accurate than one men we get the following : let us assume that the best man in stead of missing by 18 centimeters misses by 10 centimeters and the worst woman misses by 11 centimeters in stead of 6.
The difference is still significant (p=0.0227; two-tailed).

Let us try a third one : we take case 2 but make the two of the three worst men somewhat better and the second best women somewhat less accurate (men: 10,14,15,22; women 3,8,11) : it is not significant any more (p=0.0664; two-tailed)

So, even with such small samples it is perfectly acceptable to draw conclusions.

But for a data miner, who is used to work with millions of observations it still feels a bit weird !

Did you liked this post ? Then you might be interested in the following :
Howmany inputs do data miners need ?
Oversampling or undersampling ?
are men and women different ?

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A certain Kent Hovind has apparently turned a “spoof” into a serious matter. in “Opossums, Redwood Trees, and Kidney Beans” he writes (but obviously does not believe it himself) that evolution goes in the direction from few to many chromosomes. Meaning that we started as a penicillinum with two chromosomes and evolve in the direction of a fern with 480 chromosomes. Of cause totally rubbish.

Here you can find other discussions by Kent Hovind on the subject and here the wikipedia description of the man.

The question is : is evolution following a certain direction like :
- getting bigger
- having more genes
- having a larger brain
- having a larger total length of the nervous system
-

I would say : NO

Evolution is simply an adaptation to changing environments. It is the environment that dictates the direction of evolution. If it becomes colder, individuals that better resist cold are at an advantage and consequently the mean cold resistance of the population increases. If afterwards it becomes warmer, evolution is forced in the opposite direction.

Remember : evolution has no purpose whatsoever, it is only the consequence of selection, which is not random, but favors those individuals which are best adapted tot the environment.

Did you enjoy this post ? Then you might be interested in the following :
top-10 lists on evolution
The pope believes in evolution
Human evolution : the future of men
Evolution towards Intelligent Design
The end of evolution

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What is simplexity ?
Before some weeks I never heard or saw the word. It seams like cute, original, and most of all : scientific and difficult.

What is it all about ?
According to wikipedia is is an ” emerging theory that proposes a possible complementary relationship between complexity and simplicity.”
Professor Petter Wipperman (also in wikipedia) proposed a social definition :
“Simplexity therefore stands for a balance between the growing complexity of daily life and our own personal satisfaction.”

But searching a little further I found this : ” Simplexity: in systems theory a term for the emergence of simple features as a direct (though possibly highly intricate) consequence of a system of rules. Jack Cohen and Ian Stewart. “The Collapse of Chaos: Discovering Simplicity in a Complex World.” New York: Penguin, 1994. p. 399.” on Simplexity.co.uk.
So the word is not all that new.

If you read some posts on my blog, of other writings about emerging patterns, hierarchies and the like you will probably find that these definitions ring a bell : there is nothing new, only the name.
It is all about making 1) complex things simple (The romans already new : “divide et impera / divide and conquer), which is what all our analysis methods are about : cut the complex monster in simple pieces and then the whole becomes simple an 2) how you can make complex constructions with simple items or rules : think of Lego, fractals, swarm intelligence, a book written with 26 different letters, or our DNA blueprint written with 4 different nucleotides

One of the most recent examples is twitter : how such a simple (messages with 140 characters) system give birth to such a huge and complex hype, with hundreds of twitter tools and applications ?

The book

Perhaps the hype (?) about simplexity comes from a book, written by Jeffry Kluger : “Simplexity. Why simple things become complex (and how complex things can be made simple)”. J.Kluger also wrote an article about it in Time.
I must confess: until now I did not read the book, but I you believe others then here you can find more :

• Simplexity is not a simple book to read.(Prepare and share)
• Book review
• Another book review

Did you liked this post ? Then you might be interested in the following :
A bunch of tools for twitter
Do stock traders show swarm intelligence ?
The end of emergence
Evolution towards intelligent design

Image via Wikipedia

Enjoy reading !

Twitter’s Ten Rules For Radical Innovators
New Twitter Research: Men Follow Men and Nobody Tweets
Ground zero brandbuilding
Google vs. Bing: The Blind Taste Test
Google Analytics Learning
Half of your friends lost in seven years
Endless copyright free music
People who wear rose-colored glasses see more
Predictive powers: a robot that reads your intention?
Stress makes your hair go gray
Computing in the quantum dimension
Metadata floating around in the real world: allways and everywhere online !
Baseball infographics and other visual treats
Website Analytics Toolbox
Find and share logins for websites that force you to register
192 Creative, Smart & Clever Advertisements
Complexity Papers Online
Asking a machine to spot threats human eyes miss
There is much more consensus among men about whom they find attractive than there is among women
Reading the brain without poking it

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Humans have 46 chromosomes. But what about other animal species ?

There are surprisingly few comparative lists of chromosome numbers to be found on the internet. I admit : it does not make a lot of sense. What would be the scientific value of that ?

Just out of curiosity I searched a bit and as far as I know below is the only list of animal species with 46 chromosomes :

- Humans (Homo sapiens)
- Muntjacs (Muntiacus reevesi)
- Black rat (Rattus rattus) , but not all of them have 46
- European hare (Lepus europeus)
- Merriam’s ground squirrel (Spermophilus canus)
- Southern short-tailes shrew (Blarina carolinensis)
- Mountain beaver (Aplodontia rufa)
- Beach vole (Microtus breweri)
- Nilgai (Boselaphus tragocamelus)
- Kirk’s dik-dik (Rhynchotragus kirki)
- Grey vole (Microtus arvalis)

Did you enjoy this post ? Then you might be interested in the following :
Human evolution : the future of men
top-10 lists on evolution
Cost and benefits of complexity in evolution and data mining
Evolution of minerals
Evolution in blue and red

Image by compujeramey via Flickr

In physorg.com I found this post about a supermarket that “taps” energy out of the cars that drive into the parking lot.
It made me wonder : which are the other possibilities of letting others pay for your energy ?

Here are some examples I came up with. Feel free to add yours in a comment.

CARS :

• passing cars in front of your house
• at traffic lights to tap the energy at the passing cars to let the lights function.
• system to collect taxes for driving, in the form of energy, at high intensity roads,

PEDESTRIANS :

• doors in high traffic points like railway stations, shopping malls, office buildings, … could yield a lot of energy
• pedestrians : let them walk over energy-tapping rugs in shopping malls

SPORTSMEN / -WOMEN :

• fitness centers : what an energy would be produced when the fitness machines would store the energy produced by the people using it
• tennis rackets to recharge batteries, placed in the grip

OFFICES :

• keyboards button pressing, mouse movements, to power the screen or webcam, ..

I am sure there are a lot more examples to be found.

Another category is not the “stealing” of energy from people but just recycling energy that otherwise would be wasted. But that is for another post.

Did you liked this post ? Then you might be interested in the following :
Solar power ring : enough energy to fry the earth
What comes first ?
The limit of power
Science-fiction gadgets are near
No free will ?

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• Parking Lot Generates Energy (ubergizmo.com)
• Malls Seek Energy-Saving Initiatives To Cut Costs (environmentalleader.com)
• Speed bumps to create green energy (guardian.co.uk)

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Years ago I had the pleasure of working in a team of excellent people who had the habit of organizing a meeting every evening as the last part of the daily work. It was a short, quick meeting where we went over what had been done today, what where the problems to be solved, what had to be done tomorrow. Very simple but very efficient.
After this project I went back to the old rhythm of weekly, biweekly, or monthly (depending on my bosses) long boring unproductive meetings and never experienced these short but extremely efficient daily meetings again.

Last day I stumbled upon this article of Martin Fowler on Daily stand-up meetings. He gives an extensive description of how to organize these meetings, and these meetings contain everything I missed from our daily evening meetings.
It is clear from the following that these daily “scrums” as there are also called, come from the software development world.

The wikipedia definition :

“A stand-up meeting (or simply stand-up) is a daily team meeting held to provide a status update to the team members. The ’semi-real-time’ status allows participants to know about potential challenges as well as coordinate efforts to resolve difficult and/or time-consuming issues. It has particular value in Agile software development processes, such as Scrum, but can be utilized in any development methodology.

The meetings are usually time boxed to 5-15 minutes and are held standing up to remind people to keep the meeting short and to the point. Most people usually refer to this meeting as just the stand-up, although it is sometimes also referred to as the morning rollcall or the daily scrum.

The meeting is usually held at the same time and place every working day. All team members are expected to attend, but the meetings are not postponed if some of the team members are not present. One of the crucial features is that the meeting is intended to be a status update to other team members and not a status update to the management or other stakeholders. Team members take turns speaking, sometimes passing along a token to indicate the current person allowed to speak. Each member talks about his progress since the last stand-up, the anticipated work until the next stand-up and any impediments they foresee.

Team members may sometimes ask for short clarifications but the stand-up does not usually consist of full fledged discussions.”

Here is what others say about daily stand-up meetings :

- The daily stand up meeting is not another meeting to waste people’s time. It will replace many other meetings giving a net savings several times its own length. (extremeprogramming.org)

- There are plenty of other things to improve, but a daily stand-up meeting is low-hanging fruit. It is easy to implement and returns immediate gains. (codebetter.com)

- Done properly, the daily Scrum will achieve it’s own results, however handled incorrectly it can become a time wasting social hour (David’s comment on mitchlacy.com)

- Daily Scrum is a powerful tool, but as any other tool it is good, when you know what it’s useful for and have some experience in using it. … The important part is the goal, not the method. (agilesoftwaredevelopment.com)

- … how the team can synchronize their work and progress by meeting every day for a quick (15-20 min) status update and report on impediments (intranet.5amsolutions.com)

- Projects get to be late one day at a time, so it seems logical to have a daily team meeting to ensure you are all on track (www.scrumlabs.com)

- The daily stand-up meeting is a crucial aspect of keeping projects moving without interruption (www.reformingprojectmanagement.com)

- … the ability to reprioritize is one of the key strengths to a fully functioning Agile process, and having this opportunity every 24 hours is a significant benefit. (talk.bmc.com)

- There has been several occasions where the stand up meetings saved us from troubles (specially in rush hours) (Hasith comment on railspikes.com)

- the daily stand up is often the first tool to be implemented because its low cost and management can see value in it quickly. (webascender.com)

- How Microsoft’s p&p Teams do Daily Standup Meetings (ademiller.com)

I wonder if someone is using this type of meeting in another context than agile software development ?

Did you enjoy this post ? Then you might be interested in the following :
Top-10 lists on Knowledge management
Knowledge management = Change management
15 ways to use knowledge management software
The 10 most important failure factors of knowledge management.

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Look at the title of this post. Seems to be a sort of silly combination, not ?
What do these three have in common ?

I got the idea from a post entitled “Twitter and Human evolution” by Trey Ratcliff.
Trey compares the communication between tweeple (people who tweet) with communications between the cells of the human body which send short messages to eachother asking for stuff and offering some stuff.
Seems interesting.

But he concludes that these short tweets could get humanity to act as a super-organism, where people get some sort of bottom-up decision making.

OK for the bottom-up decision making, not OK for the super-organism.
First of all : we will never know if there is or will be a super-organism, just like the body cells do not know that there is a body.
Second, and not really objectively : I do not see how this could lead to a super-organism. Twitter being only a very little part of the internet it should be more likely that the internet as a whole becomes a super-organisme. But I see it largely improbable that one single organism (the internet) evolves to some super-organism with real mental capacities. Evolution uses large number of organisms, and (natural) selection to end up with something meaningful. One internet is not really a large number …

And what about stock quotes ?

Bottom-up decision making due to twitter is comparable with buying or selling stocks based on the information we find in discussion fora, in newspapers, and even on twitter. But there is a huge difference : with stock quotes we also have the actual stock quotes, which is the real result of the combined buy-sell behaviour of thousands or millions of people.

With twitter, we only have the tweets. There is no software running behind the scene to analyse for example all the tweets concerning “evolution” to come up with a global picture of what people think of evolution second by second. Offcause it would be nice to have such a service!

Enjoyed this post ? Then you might be interested by the following :
Web 5.0 : the telepathic web
Do Stock Traders show Swarm Intelligence?
Swarm versus intelligence
Piqqem : Prediction market for prediction errors
swarm-information-transfer-techniques