Drömspelet 3

Från Ett drömspel, av A. Strindberg.


De rätt-tänkande

Från Ett drömspel, av A. Strindberg.


Tid och rum existerar icke; på en obetydlig verklighetsbakgrund spinner inbillningen ut och väver nya mönster; en blandning av minnen, upplevelser, fria påhitt, orimligheter och improvisationer.

Personerna klyvas, fördubblas, dunsta av, förtätas, flyta ut, samlas. Men ett medvetande står över alla, det är drömmarens; för det finns inga hemligheter, ingen konsekvens, inga skrupler, ingen lag.

Från Ett drömspel, av A. Strindberg.

Statistics art

Hans Rosling gives us amazing demonstrations on how data and technology together can provide statistics that tells us about global context, our history and our lives.

As a good artwork, it shows us differents ways to look at the reality.

Om Statistik

Statistik = stat + vetenskap.

Vetenskapen som ger staten kunskap för att styra samhället på det mest efektiva sättet. Som i Michael Foucaults “biopolitik” koncept.

Men det var länge sedan detta var en kunskap exklusiv av staten. Idag delar företagen detta status att veta mycket om oss.

Statistik (stat + företag) > information = makt.

Sjukskriven skottar snö och blir anmäld av grannen

Förra året fick Försäkringskassan in 9 653 tips från allmänheten om misstänkta bidragsfuskare. Det utgör en majoritet, cirka 45 procent, av de impulser om fusk kassan får in.

Tipsen kommer bland annat från grannar, släktingar och arbetsgivare.

– De vanligaste fallen rör människor som jobbar svart, trots att de har någon form av ersättning. I en del fall kan det handla om människor som ser sina grannar skotta snö, trots att de blivit beviljade förtidspension för ryggvärk, säger Daniel Lundmark, verksamhetsutvecklare på Försäkringskassan.

– Det är positivt att vi får in uppgifter, exempelvis de som rör svartarbete.

Vad som ligger bakom det höga antalet tips vet inte Försäkringskassan. Men myndigheten tror inte att fler fuskar, snarare att fler har blivit benägna att anmäla fuskarna.


“If you have something that you don’t want anyone to know, maybe you shouldn’t be doing it in the first place.” Eric Schmidt, Google CEO

Google’s search for the perfect learning machine

Google’s quest to build the ultimate machine-learning system has produced some lessons of its own.

The project, code-named “Seti” in a nod to the search for life in outer space, is being used on huge data sets in an attempt to solve what Google calls “hard prediction problems.” Google revealed the name of the project in a blog post that also acknowledged the limits of engineering.

Machine learning is a favorite topic of Google co-founders Sergey Brin and Larry Page, and is useful for improving translation algorithms and semantic understanding, according to Google. But, of course, it’s an enormously complex notion, one that can intimidate even Google’s computer scientists that could make use of such a system.

“We saw very early on that, despite its many significant benefits, machine learning typically adds complexity, opacity, and unpredictability to a system. In reality, simpler techniques are sometimes good enough for the task at hand,” Simon Tong wrote.



Machine learning: the self-programming machine.

Machine learning is a scientific discipline that is concerned with the design and development of algorithms that allow computers to evolve behaviors based on empirical data, such as from sensor data or databases. A major focus of machine learning research is to automatically learn to recognize complex patterns and make intelligent decisions based on data; the difficulty lies in the fact that the set of all possible behaviors given all possible inputs is too complex to describe generally in programming languages, so that in effect programs must automatically describe programs. Artificial intelligence is a closely related field, as also probability theory and statistics, data mining, pattern recognition, adaptive control, and theoretical computer science.

By 1961 Herbert A. Simon was not alone in predicting that “… we can dismiss the notion that computer programmers will become a powerful elite in the automated corporation. It is far more likely that the programming occupation will become extinct (through the further development of self-programming techniques) than that it will become all powerful. More and more, computers will program themselves; and direction will be given to computers through the mediation of compiling systems that will be completely neutral so far as the content of the decision rules is concerned.


Cellphone traces reveal our predictability

“We are all in one way or another boring,” says Albert-László Barabási at the Center for Complex Network Research at Northeastern University in Boston, who co-wrote the study. “Spontaneous individuals are largely absent from the population.”

Barabási and colleagues used three months’ worth of data from a cellphone network to track the cellphone towers each person’s phone connected to each hour of the day, revealing their approximate location. They conclude that regardless of whether a person typically remains close to home or roams far and wide, their movements are theoretically predictable as much as 93 per cent of the time.

“The most surprising thing to us is the lack of variability in predictability across the population, meaning that most all the users have same degree of predictability,” regardless of their gender, age, or language spoken, lead researcher Chaoming Song, a physics doctoral student at Northeastern University, told LiveScience.

“For us they are just like particles in a gas that move and interact with each other,” said Song.

Read more about the study and how it was done in an FAQ on Barabási’s webpage

Science, DOI: 10.1126/science.1177170