La inteligencia artificial permite que decisiones que hasta ahora tomábamos los humanos puedan automatizarse mediante algoritmos informáticos. Aunque buena parte de esas decisiones se hallan en el campo del entretenimiento y las redes sociales, también las encontramos en las finanzas, la educación, el mercado laboral, las aseguradoras, la medicina o la justicia. Ante este fenómeno, de implicaciones sociales profundas, aparecen varias preguntas: ¿qué pasará con los puestos de trabajo asociados a esas tomas de decisiones? ¿Cómo podemos garantizar que esos algoritmos tomen decisiones justas?
This means that the move towards an algorithmically driven society also represents a radical power-shift, away from citizens and consumers and towards a smallish number of powerful, pathologically secretive technology companies, whose governing philosophy seems to be that they should know everything about us, but that we should know as little as possible about their operations.
no es falso considerar hoy en día que todos estos datos que proveemos a los gigantes de la economía digital en cada uno de nuestros actos digitales (simplemente, por ejemplo, desplazándonos con un teléfono geolocalizado), y que nos revenden luego bajo la forma de servicios diversos, constituye una de las expoliaciones del bien del pueblo más espectacular de la Historia.
The “open data” movement has produced a deluge of publicly available information this decade, as governments like those in the UK and US have released large volumes of data for general use.But the flood has left researchers and data scientists with a problem: how do they find the best data sets, ensure these are accurate and up to date, and combine them with other sources of information?
Company plans to make content generated by users available to commerce, academia and even police involved in crowd control
You are travelling by plane to see your newborn grandchild. As you board the aircraft, the cabin crew address you by name and congratulate you on the arrival of a bouncing baby boy. On your seat, you find a gift-wrapped blue rattle with a note from the airline.
In Twitter data strategy chief Chris Moody’s vision of the future, companies surprising their customers like this could become an everyday occurrence – made possible because Twitter is listening.
Computer systems are already aggregating trillions of tweets from the microblogging site, sorting and sifting through countless conversations, following the banter and blustering, ideas and opinions of its 288 million users in search of commercial opportunities.
It is not only commercial interests that are mining the data. Academics are using it to gauge the mood in a football crowd, and trying to shed light on whether Premier League players such as Manchester United’s Radamel Falcao are overpaid – with a team of researchers from Reading, Dundee and Cambridge universities testing whether top-flight footballers’ salaries are related purely to performance on the pitch or can be boosted by popularity on social media.
Selling data is as yet a small part of Twitter’s overall income – $70m out of a total of $1.3bn last year, with the lion’s share of cash coming from advertising, but the social network has big plans to increase that. Its acquisition of Chris Moody’s analytics company Gnip for $130m last April is a sign of that intent.
Google and Facebook have built their businesses around sharing data, but their control of our private and public information has become a source of huge controversy.