Image via Wikipedia

As a result of global warming, our ecosystems are running away at a speed of 420 meters per year.   That is about 500 human paces, 1.5 per day.

What does that mean ?

At first sight this is not a big deal.  What is 420 meters ?  This is only 42 kilometers in 100 years ! The animals and plants just have to follow !

Wait !

The animals, perhaps they can follow, they can walk, fly, crowl, dig …

What about the plants ? If they are lucky to have mechanisms like dispersing of their seeds by wind, or by birds (seeds in fruits), they should manage it.

No, Wait !

What if there are obstacles ?  Like seas, streams, mountains ?  Neither animals nor plants can follow their ecosystem, because the ecosystem will cease to exist.  An ecosystem that follows its preferred temperature and hits the seashore, will simply drown in the see.  You ever saw a forest crossing a sea?

What is the alternative ?

Adapt or die !
Species that follow their ecosystem do this because at the back end the environment becomes a bit too difficult to live in.  But when their ecosystem hits the sea, all that will stay behind is this unfavorable environment.  The only solution is to adapt to this new environment.  Which means : evolution, as quick as possible, before the environment becomes to harsh.

So I predict that we will witness accelerated evolutions going on all over the world.

Did you enjoy this post ?  Then you should read the following :
- Human evolution : amazingly fast
- The direction of evolution : speed matters
- Evolution can occur in less than 10 years
- The chicken or the egg ?

Related articles by Zemanta

• Species Must Run From Global Warming to Survive (treehugger.com)
• Is There a Climate-Change Tipping Point? (time.com)

Image via Wikipedia

What is to come ?

1) nano-stuff.  The potentials are huge and the technology is developing fast.  Exemples : A nano-window that washes itself or Tracking new cancer-killing particles with MRI

2) Artificial Intelligence.  While the data-mining hype in the 80-s was a failure because of computer processing limits, a new wind blows trough AI that wants to create a Superhuman Intelligence. a so-called Singularity.

3) Mind-machine communication.  This is still very basic but one success after the other is published.  Example:  people type only with thoughts.  But this means more than mind-machine communication : if you ad a second mind on the other side of the machine, you have mind-mind communication : technology-enabled telepathy !

4) electric cars : Not really future any more.  With the greener mind of a lot of people, the investments in wind- and solar energy it is just a matter of years before everyone will buy an electric one.  Mine must have photovoltaic panels on its rooftop to recharge the battery when I am shopping.

5) photo-voltaic windows : photovoltaic panels are ugly when you put them on your roof.  Photovoltaic windows are just like other windows and hence why should you not use them in stead of normal ones?  But before the technology becomes really mature, we can take pĥotovoltaism into account when designing our buildings in stead of putting the panels afterwards on our roof.

6) wireless database-driven / data mining medicine (not just doctors with gut feelings) : Do you know of examples where your doctor was wrong for months before the patient or his/her family decided to go to another doctor who saw in the blink of an eye (or after some blood tests) what the real problem was?  Now databases exist and medical software to assist the doctor.  So he should see you and, guided by his software, should ask the right questions, eliminating all impossible diseases, to come up either with the right one or with extra tests to perform in order to detect the real problem. Exit medicine men !

7) movies without movie-stars (Avatar squared) : Movie stars out of work !  1. Select the people you want to be the basis for your stars.  2. Film them in their real life.  3. Load them up in your computers.  4.  Feed the computers with the scenario/script.   5. Select the looks/feels/characteristics of your stars. 6. Describe the decors.  7. Run the software  8. Evaluate the result and eventually go back to step 4 or 5. 9. Do some editing.   10. Ship the movie.

8 ) the semantic web (Web 3.0).  It will become possible to tell your computer (smarthphone, whatever that’s connected to the internet) in your own language what you want.  It will respond not based on the words, but on their meaning (in context).

9) accelerated evolution : global warming changes the environment for a lot of species much faster than usual.  They will either follow their preferred ecosystem as it moves around, or if they encounter a serious obstacle an cannot move further, they will evolve lightning fast to the new conditions.

10) robots : Real humanlike robots like the japanese Asimo will stay too expensive for a normal human being.  But a lot of military applications are possible so things like : cockroaches offer inspiration for running robots, or flying insects and robots will go at war !

I’m not the only one to make previsions of the future :

- the futurist
- Institute for emerging ethics & technology
- Lain Dale’s diary
- Wall street pit
- ReadWriteWeb
- ZDnet
- Darwin Central
- True/Slant
- The Security Blog

Did you like this post ?  Then you might enjoy the following :

Human brain copy protection by Anymind inc.
Job interview or brain scan ?
Adam and Eve : Robot scientists
New laws of robotics
Web 5.0 : Computer telepathy ?

Do you know which are the most popular twitter tools ?

Curious as I was, how could you know such a thing ? Organize a poll ? I fear I do not have enough readers or followers on twitter to end up with sufficient data.

But I had two options left :

1. count the number of times a twitter tool appears in lists of twitter tools. You know, there are lots and lots of lists of twitter tools on the internet. A tool that appears in every list must be very popular, at least for twitter tools listers.
2. get the number of hits google returns you when you search for the twitter tool

I decided to use the second method.

But first I needed the names of “all” the twitter tools. So I started to get them from the various twitter tools lists. Soon I saw that this could be an exercise that goes on forever !
Neither my patience nor my time are endless, so I decided to stop after 15 lists and 250 twitter tools. Feel free to continue the exercise !

First of all here are the 15 lists :

Mashable
BashBosh
techcruising
Rssapplied
dailyseoblog
Brian Solis
techcrunch
The twitter toolbox
online marketeer
99 Essential Twitter Tools And Applications
Top twitter tools
Top twitter tools for business
My Top 10 Free Twitter Tools (and 3 Honorable Mentions)
47 Awesome Twitter Tools You Should be Using
Twittermania: 140+ More Twitter Tools!

( At the end of this post, I give the remaining url’s of the twitter tools lists that I did not use. )

And now the results.
But first there are two remarks to make.

1. Most searches were straightforward because the tool has some typical “twitter-like” name so you – and google – cannot get confused to mix them up with some other already existing concept. Example : splitweet.
   But there were a lot of more ambiguous names, like “hellotxt” or “glue”. In those cases I used either the website name of the tool (getglue.com) or added “twitter” to the searchterm.
2. I know the numbers that google returns are mere … “google numbers”. This means we do not exactly know what’s behind, unless we browse for example all 126.000.000 hits for bit.ly which is a bit too much for me. Also I noted that google never uses more than 3 meaningful digits, the rest are zeros. So these numbers are not very precise. But at least they give some overall picture, which is interesting to see, but, I am aware of, has not very much real meaning or values. Say it is just for fun and curiosity.

And here is the list of 250 twitter tools and their number of google search results : enjoy !
Sorry that I did not hyperlinked them all, just too much work !

1	twittersearch	402000000
2	friendorfollow	208000000
3	bit.ly	126000000
4	seesmic	15900000
5	twitter karma	15000000
6	twittangle	13070000
7	ping.fm	11900000
8	cotweet	9520000
9	twittercounter	8080000
10	tinyurl	7760000
11	brightkite	7470000
12	timer	6780000
13	hootsuite	6050000
14	wefollow	5330000
15	twitthis	4800000
16	headup	4230000
17	tweetmix	4200000
18	toro for twitter	4050000
19	twitterfeed	4030000
20	diigo	3730000
21	loopt	3550000
22	twitter grader	2800000
23	twitpic	2680000
24	tweetdeck	2170000
25	tweetmeme	1800000
25	twitterrific	1750000
27	splitweet	1040000
28	quitter	1020000
29	cheaptweet	841000
30	strawpoil	767000
31	hellotxt	765000
32	twitalyzer	669000
33	snipurl	654000
34	twitterfriends	650000
35	magpie	588000
36	twiggit	581000
37	hashtags	539000
38	twitwall	539000
39	tweepsearch	537000
40	emailtwitter	522000
41	Doesfollow	517000
42	twhirl	490000
43	twitgraph	476000
44	rememberthemilk	461000
45	twitxr	457000
46	hoopla	451000
47	tweetcloud	436000
48	retweetist	425000
49	destroytwitter	404000
50	twibs	397000
51	glue	393000
52	tweetree	375000
53	twellow	353000
54	twitt twoo	349000
55	spaz	326000
56	tweet2tweet	320000
57	twitterberry	291000
58	tinytwitter	290000
59	summize	284000
60	snitter	278000
61	twitterfon	271000
62	twitscoop	267000
63	retweetrank	261000
64	twitterfall	259000
65	favrd	256000
66	microplaza	253000
67	outwit	250000
68	digsby	247000
69	twitterreply	244000
70	twist	236000
71	flaptor twitter search	227000
72	twitter search firefox	222000
73	jott	219000
74	twitbin	218000
75	twittelator	211000
76	twitterkeys	189000
77	twitterlocal	188000
78	twitterbadge	179000
79	twitterholic	178000
80	powertwitter	176000
81	twibble	174000
82	twinbox	166000
83	tweetvisor	163000
84	easytweets	159000
85	tweetrank	157000
86	bubbletweet	154000
87	backtweets	150000
88	huitter	148000
89	tweetstats	145000
90	Itweet	142000
91	tweetbeep	142000
92	twitterfox	142000
93	tweetlinks	135000
94	slandr	131000
95	twitterless	126000
96	vakow	124000
97	twittervision	122000
98	twitdir	118000
99	twitzer	116000
100	twtpoll	114000
101	twitterfone	113000
102	twitter2go	111000
103	twittermail	110000
104	tweetvolume	108000
105	twitdom	103000
106	mrtweet	96300
107	twittytunes	93200
108	tweetlater	92900
109	peoplebrowsr	90100
110	twinfluence	88100
111	twitternotes	87500
112	twideoo	87000
113	mr milestone	86100
114	cursebird	86000
115	WP twitter tools	85700
116	tweetgrid	82600
117	tweetr	82000
118	twoogle	81000
119	twitterbar	80700
120	snaptweet	72700
121	tweetscan	71300
122	twitteroo	68700
123	hahlo	68500
124	tweetburner	68200
125	twuffer	66800
126	twittercal	65100
127	twittonary	65100
128	twitter updater	63400
129	tweetchat	62900
130	twitter100	62800
131	tweetake	60800
132	socialtoo	60600
133	nearbytweets	56900
134	monitter	56600
135	tweepler	55900
136	twtvite	55200
137	twilert	55100
138	tapulous	52300
139	tweetwire	50900
140	feedalizr	50700
141	secrettweet	49800
142	twitterhawk	48400
143	twitturly	48400
144	Xpenser	48400
145	grouptweet	48300
146	tweetcube	48000
147	tweet this	47900
148	tweetwheel	46700
149	linkbunch	46400
150	twiddict	46200
151	twittertise	44900
152	tweetsum	43500
153	twitstat	43200
154	followcost	43100
155	twitter sharts	41900
156	tweetrush	40300
157	untweeps	38700
158	twtqpon	37900
159	tweetsuite	35600
160	citytweets	35500
161	twistori	35100
162	twitpay	34800
163	twitterpatterns	33200
164	tweepular	32300
165	gps twit	31800
166	twonvert	30900
167	Matt	30500
168	livetwitting	30400
169	twitseeker	30000
170	twittergallery	30000
171	twitoria	29100
172	quotably	28400
173	mymilemarker	28100
174	tweetchannel	27800
175	twistory	27800
176	tweet pro	27400
177	twitzu	27000
178	justtweetit	26500
179	twitterIM	26100
180	gridjit	25700
181	twittercamp	25100
182	twubble	24800
183	socialwhois	24700
184	twittereyes	23500
185	twtrfrnd	23400
186	twittad	23200
187	twittersnooze	21900
188	brabblr	21400
189	twalala	20800
190	whostalkin	19700
191	twitsay	19500
192	twittearth	19400
193	istwitterdown	19300
194	twtcard	19200
195	pockettweets	19000
196	toptweet	18900
197	twerpscan	18600
198	nozbe	17500
199	twemes	17000
200	autopostr	16300
201	twixxer	16200
202	twitterdigest	16100
203	whoshouldifollow	15900
204	twithire	15800
205	madtwitter	15300
206	tweetwhatyouspend	15000
207	xefer	14500
208	twittertroll	13800
209	twitterlights	13000
210	twitority	12200
211	twitterfriends network browser	12100
212	feedtweeter	10100
213	tweetwasters	9870
214	tweetie for iphone	9680
215	twitrans	9520
216	twiffid	9200
217	mytweeple	9130
218	twitresponse	9110
219	itweet2	9020
220	microrevie	8970
221	twitterratio	8960
222	Nest.Unclutterer	8730
223	twply	7730
224	itwtr	6730
225	twitspy	6320
226	postica	6260
227	twitrand	5660
228	twinkle	5520
229	dreamtweet	4870
230	whatsyourtweetworth	4190
231	mycleenr	4090
232	WP twitip Id	3780
233	twitslikeme	3760
234	alphatwitter	3510
235	plodt	3180
236	readmytweets	3150
237	twittords	3020
238	vacatweet	3010
239	twenglish	2850
240	acamin	2420
241	tweetpad	2330
242	twitexplorer	2120
243	twi8r	1880
244	twitgeistr	1880
245	whofollowswhom	1660
246	tweeterology	1410
247	twitblocker	1150
248	twitalks	1040
249	twitterscan	39

 
Oops ! either I come one too short, or I made some mistake in my numbering …

And here are the url’s of the other twitter tools lists I did not use :

http://www.sociableblog.com/2009/03/18/100-twitter-tools-to-help-you-achieve-all-your-goals/

http://net.tutsplus.com/articles/10-awesome-ways-to-integrate-twitter-with-your-website/

http://www.folkd.com/go/top+10+twitter+tools

http://www.ruhanirabin.com/top-8-most-useful-practical-twitter-tools-for-the-twitter-professionals/

http://www.1stwebdesigner.com/development/27-twitter-tools-to-help-you-find-and-manage-followers/

http://www.quickonlinetips.com/archives/2007/04/10-best-twitter-tools-for-wordpress-blogs/

http://www.seoptimise.com/blog/2009/10/30-twitter-tools-for-business.html

http://tendou86.blogspot.com/2009/01/top-10-twitter-tools.html

http://www.hellogiri.com/top-10-most-useful-twitter-tools-list-for-pc-mobiles-and-blogs/

http://www.honeytechblog.com/top-25-twitter-tools-for-wordpress/

http://www.newmediabytes.com/2008/01/18/best-twitter-tools-resources-and-clients-guide/

http://savethemedia.com/2009/02/17/top-twitter-tools-for-journalists/

http://www.noop.nl/2009/06/top-100-most-influential-twitter-tools.html

http://www.google.be/search?hl=nl&client=firefox-a&rls=com.ubuntu:nl:official&q=TOP+10+LIST+OF+twitter+tools&start=30&sa=N

http://www.bestcollegesonline.com/blog/2009/04/02/top-100-tools-for-the-twittering-teacher/

http://www.c4lpt.co.uk/recommended/

http://www.squidoo.com/twitterapps?utm_campaign=direct-discovery&utm_medium=sidebar&utm_source=pkmcr

http://pelfusion.com/tools/30-twitter-tools-for-managing-followers/

http://twitter.com/Top10TwitTools

http://www.seodubai.org/2009/01/16/list-of-twitter-tools-that-you-must-have/

http://brendanhughes.ie/2009/06/21/top-10-twitter-tools-for-business/

http://www.smbceo.com/2009/03/25/top-27-twitter-applications/

http://www.smmguru.com/2008/10/22/the-master-list-of-twitter-tools-and-apps

http://www.hypebot.com/hypebot/2009/04/10-top-twitter-tools-tips-and-tricks-for-musicians.html

http://www.socialmediatoday.com/SMC/80437

http://www.blogcatalog.com/topic/list+of+twitter+tools/

http://www.scgpr.com/wordpress/?p=492

http://www.socialmedialists.com/wiki/index.php?title=Twitter_Tools

http://www.twitadder.info/

http://www.thedailyanchor.com/2009/02/17/85-twitter-tools/

http://www.techtreak.com/downloads/10-awesome-twitter-tools-as-wordpress-plugins/

http://steve-wakefield.com/2009/10/my-top-10-twitter-tools-and-then-some/

http://www.thinktechno.com/2009/05/31/top-10-twitter-tools/

http://www.brandsamongmany.com/2009/03/09/the-ultimate-list-of-twitter-tools/

http://www.webuildyourblog.com/1289/increase-twitter-top-10-twitter-tools/

http://www.networkworld.com/slideshows/2008/060208-top-twitter-tools.html

http://www.warriorforum.com/blogs/dsmpublishing/8167-top-10-twitter-tools-everyone-should-own-their-online-business.html

http://www.girlopinion.com/2009/06/07/top-10-twitter-tools/

http://www.twitip.com/10-more-must-have-twitter-tools/

http://gnoted.com/100-twitter-tools-ultimate-power-collection/

http://freelancefolder.com/15-useful-twitter-tools-for-web-workers/

http://www.optimalwebworks.com/web-business-marketing/twitter-primer/

http://www.placona.co.uk/blog/post.cfm/my-top-favourite-twitter-tools

If you enjoyed this post, then you might also be interested in the following : top 10 lists of twitter tools A bunch of tools for twitter A second bunch of tools for twitter Micro Email = twitmail

 

Image via Wikipedia

Twitter started in March of 2006 as a very simple service to connect people by sending short messages of max. 140 characters.
Who could imagine at that time that not only twitter would become so popular, but that, thanks to their API the number of twitter tools and services would explode the way it did ?

On the internet we find a wealth of lists of twitter tools (I wrote also two of them). As the evolution rocks off the charts, and I wanted to assemble a new list I figured it would be interesting to make a meta-list : a list of lists of twitter tools.

We can find all sorts of lists of twitter tools. I figured the lists I wanted to list had to have something in common, so the list would make some sense.

It has become a list of top-10 lists :

1	BashBosh : Top 10 Tools for Twitter Freaks
2	Techcruising : Top 10 Twitter tools for a power user
3	Rssapplied : Top Ten Twitter Tools
4	Dailyseoblog : 10 twitter tools to effectively manage your followers
5	The twitter toolbox : Top 10 Tools For Your WordPress Blog
7	Itpro : Top 10 Twitter tools for business
8	Dooleyonline : My Top 10 Free Twitter Tools (and 3 Honorable Mentions)
9	Tutsplus : 10 Awesome Ways to Integrate Twitter With Your Website
10	Atniz : Top 10 Twitter Tool
11	Quickonlinetips : 10 Best Twitter Tools, Plugins, Widgets for WordPress Blogs
12	Tendou86 : Top 10 Twitter Tools
13	Hellogiri : Top 10 Most useful Twitter Tools list for PC, mobiles and blogs
14	Top10 Twitter Tools : Twitter Tools Top 10
15	Brendanhughes : Top 10 Twitter Tools for Business
16	Hypebot : Top 10 Twitter Tools For Musicians
17	Techtreak : 10 Awesome Twitter Tools as WordPress Plugins
18	Steve-wakefield : My Top 10 Twitter Tools… and then some!
19	Thinktechno : Top 10 Twitter Tools
20	Webuildyourblog : Increase your Twitter following with these top 10 Twitter Tools
21	Warriorforum : Top 10 Twitter Tools That Everyone Should Own For Their Online Business
22	Girlopinion : Top 10 Twitter Tools
23	Twitip : 10 MORE Must Have Twitter Tools

.

If you enjoyed this post, then you might also be interested in the following : A bunch of tools for twitter A second bunch of tools for twitter Micro Email = twitmail

 

Enjoy browsing :

Douglas Hofstadters: musing on the singularity
a clever way of searching
how to really browse without a trace
you should follow me on twitter
The new era of inbound marketing
the twitter song
Top 10 Most useful Web Developers tools for Firefox
elevators to space
Sharing small snippets of information about your daily life could be generated automatically
who will edit your life ?
A Fractal Perspective on Enterprise 2.0 Adoption
10 things about google that you might not know
test your science knowledge with science cheerleaders (fun)
you think your child is smart ?
what is the meaning of “organism” ?
how ants make their nest
periodic table of marketing elements
Bill Bryson’s Notes from a Large Hadron Collider
The Über-Connected Organization: A Mandate for 2010
Is neighbor’s Wi-Fi signal free for me to use?
dark chocolate helps ease emotional stress
Why does’nt linux need defragmenting ?
Are solar cells warming up the earth ?
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graphedge
six insane laws we will need in the future
explore your twitter friends and hashtags with mentionmaps
Top Ten list of excuses not to engage in co-creation
how to achieve something
how heavy is the internet ?
Intel wants a chip implant in your brain
in the brain, se7en is a magic number
We perform best when no one tells us what to do

Enjoyed this post ? Then you might be interested in the following :
link list for october 2009
link list for september 2009
link list for august 2009
link list for juny 2009
link list for may 2009

Image via Wikipedia

I am sure everybody knows the feeling when you get stuck in a traffic jam. No need to say this is becoming a huge problem.
Why are there traffic jams ? Is it possible to prevent them ?

What is a traffic jam ?
Very simply put : you experience a traffic jam, when there is no space in front of you to move on. We all love an empty road ahead. But you do not really need an empty road in front of you. When the driver in front of you nicely drives on, he is constantly making the necessary space so that you can move on too.
So there are two factors : i) there is a car in front of you and ii) it is not moving.
(“Ants have no traffic jams !” Are they more intelligent ?)

How much space do you need ?
This is not so simple. It depends on your speed. You want enough space to have the time to stop when the one in front of you stops. Hence you only move on when there is more space before you than the minimum you feel save with. What you really want is not space, but time. A good -conservative- rule of thumb is 4 seconds or 2 crocodiles (just say : “one crocodile, two crocodiles”).

To put it the opposite way : When is there no traffic jam ?
First everybody must be moving, and second there has to be enough time between the cars.

How to prevent traffic jams?
Since there are two factors in play : space and speed (space/time) we can play with both.

i) The first is space : it is obvious that lowering the number of cars on a given time on a given road will be a good thing, making more room per car. So you need to prevent (some) people to take their car, for example by enhancing public transportation, by making it more expensive to drive a car (taxes).

ii) The second one is less intuitive : a general remedy to traffic jams is limiting the speed. Why ?
My first reaction is : this makes no sense at all! If at a high speed or a low speed you allways keep 4 seconds between two cars, this means that either way every 4 seconds there is a car. So at a lower speed the road cannot “transport” more cars per time-unit.
However, there is another consequence of driving slower : the space you need in front of you diminishes : 4 seconds at 70 km/hour means that you need 77.8 meters, but at 120 km/hour you need 133.3 meters. So the effect of speed limitation is that the road can contain a lot more cars : 12.8 per kilometer at 70 km/hour, compared to only 7.5 per kilometer at 120 km/hour.
So either lowering the number of cars or limiting the speed leads to the same consequence : it prevents saturation of the roads. However, from the moment on that the road is saturated, the same traffic jam misery will start again.

iii) A third solution would be to lower the distances between the cars without changing the speed. Sure, there would be a security problem, unless everybody becomes an extreme alert driver (like the formula 1 people). A (future ?) solution is electronics. We can easily imagine a device with sensors to keep automatically a minimum distance. In stead of our automatic cruise control, we could switch to automatic distance control : This already exists ! I remember there has been an experiment like this with trucks, with only one driver in the first one and the other trucks simply automatically follow everything the first did, just a few meters separated from one another. Here is a more recent article on a similar subject.

And what about the “mystery of traffic jams” or “phantom traffic jams” ?
This is not really a mystery or a phantom, it’s just the result of a saturation of the road and the behaviour of the drivers.

Anyway : the best way not to get stuck in traffic jams is to stay at home !

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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evolution in a bottle
nearby future view of real artificial intelligence
is the Higgs boson sabotaging its own discovery ?
if Matrix was programmed on windows XP
head or tails : not 50-50
Neuroimaging Of Brain Shows Who Spoke To A Person And What Was Said
The weirdest clouds you’ll ever see !
IBM’s twitter strategy
a robot skiing downhill
5 mind-blowing webstats you should know
what will the web look like in 5 years ?
muscle-based PC-interface

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
The pope believes in evolution
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 ?

Image via Wikipedia

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 ?

Image via Wikipedia

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