Pixel Painter is a documentary about how the most silly thing, Microsoft paint, can become the way of an old man of beating his age and makes amazing art. Hal Lasko is about 97-year-old, he was a former graphic artist who started creating in Microsoft Paint when he lost his eyesight. Just watch it.
Today I bring you a quite nice web application to explore science and relation between different sciences.
This web has an amazing and interesting tools inside.
The first of it are the sciences maps (go to the web to see more). This maps show you how different articles from different sciences cite each others.
I think some of my complex networks colleages will find interesting to have the data to perform some analisis. For example, if this network has power law in his degree distribution, it means that it is resistant to deleting some nodes randomly. In real life, that means that science will continue even if for a certain time there is no new improvements in some areas (deleting some of the nodes).
I wish to have a look to the info and plot the degree distribution, but unfortunately, to get the raw data one has to pay to Thomson Reuters.
Other tool inside this web is the citation paterns.
This allows you to see where the citations came from to an specific topic and (with colors) which was the journal that publish it.
Those are just two examples of the things inside the web. There is a lot of more kind of plots and info. Take a break and check them out. Without any kind of doubt, playing around a little bit with this web is interesting to understand the relations of your interests with other areas. And if you are looking for some especific information, this will be a good way of knowing where to find that information.
And as a final…
EigenFACTOR is close related to this other web:
This webpage is basically a big JAVA application to plot your networks and make them look as cool as eigenFACTOR ones.
As an example, I’m going to show you how to plot the network of Protein-protein interaction in budding yeast. I know, I know, there more much more interesting, but this one is free, so don’t blame at me. (Thanks to Max Stirner for helping me a little bit with this part).
Step 1. Get the data of the network. It must be in Pajek format. So you can just type “pajek network download” in google and you will get lots of examples. In my case, I found this one:
You can choose another one if you wish. Here you can find links to the most popular data sets for networks. Most of them in Pajek format or with instructions to turn them into pajek.
Step 2. Once downloaded the data, go back to Mapequation, to section “Mapgenerator“.
Step 3. In the lower part of the page you have a short tutorial of how to use it. For now, just click on Load network>Open File. This one I’m using is undirected. Look for the files you downloaded. In my case YeastS.net.
Step 4. Wait until it is loaded, and then click on calculate clusters. As you can see, the result is a little bit… Well, not impresive… but go to next step!
Step 5. To change the number of nodes displayed, go to Placement tools and play with the parameters. (In my case, my poor netbook can only show 35 nodes, above that it starts to suffer). And voilá:
Using this kind of tools researchers can look for missing relations (for example a part of the network has a low degree distribution, below the average, suggesting undiscovered relations that must be discovered and added to the graph to make it complete).
Hope you enjoy this resources and if you wish to look for more knowledge, don’t forget to visit the Barabási Lab.
I can’t remember the last week I stayed sober for more than a week… I can’t remeber the last time I though I was not dead. Because that’s how i feel. I feel that I die long time ago and this living is not living, is a dream that never ends, like a third person game where I control myself. Playing in exploration mode without any quest. No evil lord, no princess to rescue, just an open world to explore.
Why I drink? Because there is one point after getting drunk where I feel like there is no dreaming and can get real control again. But it’s just an ilusion, and continue my jumping from a computer to another one. From one piece of data to a new collection of bits. Just one more line of code, that’s what I am.
After a while, you realize that all the people are the same, just efficient control systems. From blood acideness to protein levels, from hunger to driving a car, from relationships to manage a bank balance, we are just eficient control systems.
And… and when you think that you are not more than a control machine, you start to be free. Because you can now ask yourself things like, why you see that girls in the street whyle coming home hitting a bus stop and decide to do what you did instead of anything more? What makes you behave in that maner and no take another action? If there is allways a reason for your actions what is that reason when you don’t know it? Can I decide to break my own rules? What happens If I decide to go against myself?
Actually, control theory has classes for controlers, and you can find yourself ass a kind of controller with feedback (you get your response, you know what your response want to be, and you try to improve it until you get the response you want to have). So… you take actions in response to things (like going to the kitchen in response to hunger, or going work in response to the necesity of buying things… probably because of hunger again). And even when ypu think there is no reason for it, there it is. The most hidden of all… you do things because you think they are good. Your objetive is getting food, getting funny things to not get bored, sleep, and sex. And in a more complex level, your control will take you to control difficult social situations where you are involved and where you expect to find new friends or improve relationships with old ones.
So… caming back to me. That’s the reason I drink. because I’m a controller that has no objetive. My error function is lost and have no good response to meet. How i loose it, and why I’m not going to have it anymore, is more complicated. But that’s the reason.
Or maybe I just need to be completelly broken before caming back again…
Almost forgot. Today I officially achive one of my dreams. I wanted to be in physics books before finishing my degree… because any especial contribution. Well, now I’m not yet a Ph.D. but part of my work is yet used to teach another students. So maybe the reason I get drunk is because the only way of finding new places is getting lost and reach places no one knows.
There is a novel by Thomas Mann called the Magic Mountain. In this novel a foreign visitors reaches a hospital in the mountains, a place to help people recover and get new energies. Day by day the life in the hospital is getting him and one day he became a patient itself. The life is easy in the hospital, everything has a timetable and living with the other patients is cool. There is no need for fight in this kind of life, just keep calm and follow the rules. Once you are in you can’t leave because noone feels itself is ready to be healthy again. This people suffer not from his body, but for his mind. They can’t be normal people again because this illness, this stay in the mountain makes them feel special, being part of a comunity.
This is the feeling I got now leaving Trieste. I have been granted by UNESCO to be at a summer School and Workshop about machine learning. The day I arrived I was a foreign to this kind of life, now that I left I only think in the way of coming back again.
This place is special, many people from different countries meet here and they share knowledge… and most important, they share time together and meet different cultures in equal conditions. One realizes that this place is a sharing place once he visits Marie Curie’s library and Abdus Salam’s office. Abuds Salam, nobel prize… no not nobel prize, because you find the nobel in a not special possition, it’s only one more prize for him. What is important for him? For example his newyear card from Landau, or his greetings from different friends, that’s important for Salam. He is not a man of medals, he is a man of men, and that is waht you find in this place. No nation is more important than others, no one is less.
And this is what we must learn. Machine learning is important, knowledge is important, but people is more important. And knowing that there are many people around the world with different stories working in the same subject as you. Once you realize that and start to understand what kind of things are important for them, you find out where this subject is going to be in the next years.
Machine Learning. What is this about? In a sentence, it’s about how to make machines solve problems we don’t know how to solve. I explain myself, don’t worry. Machine learning is the name we give to several different techniques to work with data that we can’t find out a model where it came from.
If you can build a model (deterministic o r not, dosen’t matter),a and make this model fit the data you have, then you are not doing machine learning. Machine learning is when you have data and there is no model which can explain it. Maybe the model exist but you can’t find it, maybe there is no model, this dosen’t matter. In machine learning we deal with data and how to extract from data different information that allows us to understand what is happening or what deccision we must take.
For example, take data from DNA, presence or abscence of different genomes. We think there is a relation between this data and different illness, and most important, we think that with data we can predict if someone is going to have this illness or not. What we can do to find out this relation? The traditional aproach is to try to find out the undercover relation between DNA and different processes inside the cell, and once we have that, find out what happens when something dosen’t work as it is supposed to work. Unfortunately, this way is too difficult, because the reactions taking place are so many and so complex. But don’t worry, we have machine learning. Give enough examples of data and illness and we can start to find relations in data, finding for example that if you build up a new variable then it became obvious if one is going to be illness or not. (You maybe think this is easy, but think about it again, maybe the data has 25 dimensions and building another one dimension reveals the information, but how to build this dimension? If you just try to combine existent dimensions you can be here forever… a technique is needed). This kind of things about clasification are done usually with a technique called Support Vector Machines.
Another example. We know how to make computer programs for many different things, from adding two numbers to playing a movie. Thousands of code lines are being written each moment. But, do we know for example how to make a computer recognizing faces? The answer is not. We don’t know how to program a computer to see faces. The answer came again from machine learning. Using machine learning you can build a program where you start giving it data, different faces, and it start to learn which things make each face be different. Obviously, if you help with some preprocessing it will work better, but the important thing is that you can train a program to recognize faces using machine learning.
One of the professors states out what is happening now in the world and why things like google are changing our way of living. “For nearly 30 years we believe that AI relies on symbolic logic rules, and we didn’t find a way to do nothing. Now we can do it, how this happened? The answer is that we forget about logic and symbolic derivations, we just use simple rules and lots and lots of data, and it works! The AI don’t relay in logic, it relays in examples!”
So as long as we relay on examples, take my example and meet different people, learn from them and try to understand why they live the way they do.
Título original: Emergence. The Connected Lives of Ants, Brains, Cities and Software.
Título de esta edición: Sistemas Emergentes. O qué tienen en común hormigas, neuronas, ciudades y software.
Autor: Steven Johnson.
Editorial: Turner Publicaciones
Edición Año: 1ª 2008
Sinopsis [Aviso: Spoiler]: El libro empieza repasando conocimientos básicos sobre sistemas emergentes. Comienza con e ejemplo de las hormigas y empieza a explicar como entidades individuales con reglas muy básicas pueden dar lugar a estructuras muy complejas. A continuación hace una interesante revisión sobre teorías emergentes aplicadas a ciudades y el papel que tiene el trato directo entre personas. También introduce a la ciudad como una especie de organismo, quizás no uno vivo, pero si un organismo que evoluciona en el tiempo y mantiene ciertas estructuras pese a su evolución.
A continuación hace un repaso al papel de las neuronas en el cerebro y cómo ese es otro ejemplo de entidades simples que dan lugar a sistemas complejos. Repasa sobretodo el hecho de que el cerebro más que una máquina de razonar es una máquina de asociar. Que realmente los pensamientos ocurren por asociaciones y que el razonamiento lógico es algo mucho más lento que hemos obtenido despues, cuando el cerebro alcanzó ya cierto tamaño y complejidad.
El siguiente punto que toca son los mecanismos de retroalimentación. Empieza planteando que la retroalimentzación es la parte clave de los sistemas emergentes y que es la que permite que exista regulación y control. Tanto a las hormigas como a las ciudades la retroalimentación es la que les permite no crecer demasiado y agotar los recursos a su alrededor o aprobechar épocas de gran cantidad de recursos para crecer.
Por ultimo toca un tema que es realmente nuevo en este área (que a su vez es también nueva). El software, los programas de ordenador y en especial los juegos. Los juegos permiten simular sistemas emergentes en nuestros ordenadores y de alguna manera controlarlos, pero en sí mismo el desarrollo de software y la interacción con el usuario hace que sea un sistema emergente con feedback.
Valoración personal: Para valorar el libro he tenido que revisar las notas que fuí tomando cuando lo leí. La primera de ellas me lleva a un parrafo donde el autor empieza a hablar de la emergencia en juegos… y pone como ejemplo…. Zelda Ocarina del Tiempo. Despues de esto sólo puedo decir que me ha ganado.
Es un libro divulgativo pero que plantea cuestiones interesantes. Por ejemplo se pregunta cómo evolucionarán las interfaces en los programas. ¿Es posible que un programa aprenda de su usuario y modifique su programación para adecuarse más a ese usuario? Algo de esto ya lo estamos viendo con google y sus busquedas personalizadas según nuestro perfil de busquedas anteriores. ¿Llegaremos a tener perfiles de usuario que nos brinden sistemas operativos totalmente personalizados? Son cuestiones interesantes y cuya respuesta parece ser que sí. El único problema es cómo llegar a ello. Para los que no estén muy interesados en ordenadores, ¿qué tal un coche que se va adaptando a su usuario y por ejemplo en vez de ofrecerle una aceleración lineal le ofrece una aceleración más acorde a su estilo de conducción? Eso será posible dentro de poco.
Otro pensamiento interesante que he apuntado leyendo el libro es que todos los observadores, debido a que reciben datos distintos y a su funcionamiento interno tienen visiones distintas de la realidad. Saber que esto es así no resulta trivial, y proporciona una gran ventaja en el mundo a nuestro alrededor. Por ejemplo a la hora de explicar cosas a otras personas, de intentar convencerlas para un negocio, o para captar su atención.
La parte sobre las ciudades resulta tremendamente interesante, ya que plantea a las ciudades como seres vivios con una vida tremendamente larga, incluso de cientos de años o miles. Explicar cómo regulan el tráfico de mercancias, cómo los barrios crecen, permanecen, evolucionan, las limitaciones de recursos… resulta apasionante.
Creo que es un muy buen libro de divulgación que puede resultar interesante para toda clase de público. Sobretodo estimula hacia nuevas preguntas.
Today I came across a curious thing, Mozart’s Dice Game.
According to wikipedia, a Musical Dice Game is a game to roll dices and create random music. (I found this game reading how a neural network was trained to generate Bach’s music).
There are several Musical Dice Games created by different compositeurs, but the most famous of all is, of course, Mozart’s one.
Mozart musical dice game was published one year after his dead by his publisher. This generates some doubts about game’s creator.
“The idea was to compose a 16-measure “waltz” by rolling dice to decide which measures to select from a large pool of choices.
In the “Musikalisches Würfelspiel,” the measures are numbered from 1 to 176, and the numbers are arranged in two charts, each consisting of 11 rows and eight columns. To select the first measure, a player would roll two dice, subtract 1 from the total, and look up the corresponding row in the first column of the first chart to determine the appropriate measure number. Subsequent rolls of the dice decide which measure to select from each successive column to complete the melody.”
Here we can see an example.
In Mozart’s version you can generate 45,949,729,863,572,161 different waltzs, so each time you generate one it will be played for first time.
Looking on google i found this page where you can download a zip with the exe file to play Mozart’s Musical Dice Game.
Please, take a while to play the game, you will enjoy it. :)
This is what I have made.
Now want to read: Gardner, M. 2001. Melody-making machines. In The Colossal Book of Mathematics. New York: Norton.
Ivars Peterson’s Math Trek: http://www.maa.org/mathland/mathtrek_8_21_01.html
Mozart Dice Game: http://www.amaranthpublishing.com/MozartDiceGame.htm
Jim Jubak, “La máquina pensante”, Ediciones B, (1993).