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== Why contextual stories? ==
 
== Why contextual stories? ==
 
During the monthly meetings of Algolit, we study manuals and experiment with machine learning tools for text processing. And we also share many, many stories. With the publication of these stories we hope to recreate some of that atmosphere. The stories also exist as a podcast that can be downloaded from http://www.algolit.net.
 
During the monthly meetings of Algolit, we study manuals and experiment with machine learning tools for text processing. And we also share many, many stories. With the publication of these stories we hope to recreate some of that atmosphere. The stories also exist as a podcast that can be downloaded from http://www.algolit.net.
 
The world of machine learning is very Anglo-Saxon. During sessions of Algolit, we communicate in English. Each one of us flavours the space with personal accents of Dutch, French, Romanian, Spanish, German. For this exhibition we decided that our wordings have their own right to exist, also when they are written down. It is a way to claim the poetic nature of the so called 'pidgin English' that is spoken by 90% of the world population.
 
  
 
For outsiders, algorithms only become visible in the media when they achieve an outstanding performance, like the Alpha Go. Or when they break down in fantastically terrifying ways. Humans working in the field though, create their own culture on and offline. They share the best stories and experiences during live meetings, research conferences and yearly competitions like Kaggle. These stories that contextualize the tools and practises can be funny, sad, shocking, interesting.  
 
For outsiders, algorithms only become visible in the media when they achieve an outstanding performance, like the Alpha Go. Or when they break down in fantastically terrifying ways. Humans working in the field though, create their own culture on and offline. They share the best stories and experiences during live meetings, research conferences and yearly competitions like Kaggle. These stories that contextualize the tools and practises can be funny, sad, shocking, interesting.  

Latest revision as of 08:43, 4 March 2019

Why contextual stories?

During the monthly meetings of Algolit, we study manuals and experiment with machine learning tools for text processing. And we also share many, many stories. With the publication of these stories we hope to recreate some of that atmosphere. The stories also exist as a podcast that can be downloaded from http://www.algolit.net.

For outsiders, algorithms only become visible in the media when they achieve an outstanding performance, like the Alpha Go. Or when they break down in fantastically terrifying ways. Humans working in the field though, create their own culture on and offline. They share the best stories and experiences during live meetings, research conferences and yearly competitions like Kaggle. These stories that contextualize the tools and practises can be funny, sad, shocking, interesting.

A lot of them are experiential learning cases. The implementations of algorithms in society generate new conditions of labour, storage, exchange, behaviour, copy and paste. In that sense, the contextual stories capture a momentum in a larger antropo-machinical story that is being written at full speed and by many voices.

We create 'algoliterary' works

The term 'algoliterary' comes from the name of our research group Algolit. We exist since 2012 as a project of Constant, an organisation for media and arts based in Brussels. We are artists, writers, designers and programmers. Once a month we meet to study and experiment together. Our work can be copied, studied, changed, and redistributed under the same free license. You can find all information on the http://www.algolit.net.

The main goal of Algolit is to explore the point of view of the algorithmic storyteller. What kind of new forms of storytelling do we make possible in dialogue with these machinic agencies? Narrative points of view are inherent to world views and ideologies. Don Quichote, for example, was written from an omniscient third person point of view, showing Cervantes’ relation to oral traditions. Most contemporary novels use the first person point of view. Algolit is interested to speak through algorithms, and to show you the reasoning of one of the most hidden groups of our planet.

Writing in or through code is creating new forms of literature that are shaping human language in unexpected ways. But machine Learning techniques are only accessible to those who can read, write and execute code. Fiction is a way to bridge the gap between the stories that exist in scientific papers and technical manuals, and the stories spread by the media, often limited to superficial reporting and myth making. By creating algoliterary works, we offer humans an introduction to techniques that co-shape their daily lives.

What is literature?

Algolit understands the notion of literature in the way a lot of other experimental authors do: it includes all linguistic production, from the dictionary to the Bible, from Virginia Woolf's entire work to all versions of Terms of Service published by Google since its existence. In this sense, programming code can also be literature. The collective Oulipo is a great source of inspiration for Algolit. It stands for Ouvroir de Litterature Potentielle. In English, this becomes 'Workspace for Potential Literature'. Oulipo was created in Paris by the French writers Raymond Queneau and François Le Lionnais. They rooted their practice in the European avant-garde of the 20th century, and the experimental tradition of the 60s. For Oulipo, the creation of rules becomes the condition to generate new texts, or what they call potential literature. Later, in 1981, they also created ALAMO - Atelier de Littérature Assistée par la Mathématique et les Ordinateurs, or Workspace for Literature assisted by Maths and Computers.

An important difference

While the European avant-garde of the 20th century pursued the objective of breaking with conventions, members of Algolit seek to make conventions visible.

'I write: I live in my paper, I invest it, I walk through it.' This quote of Georges Perec in Espèces d'espaces could be taken up by Algolit. (Espèces d'espaces. Journal d'un usager de l'espace, Galilée, Paris, 1974)

We're not talking about the conventions of the blank page and the literary market, as Georges Perec did. We're referring to the conventions that often remain hidden behind interfaces and patents. How are technologies made, implemented and used, as much in academia as in business infrastructures? We propose stories that reveal the complex hybridized system that makes machine learning possible. We talk about the tools, the logics and the ideologies behind the interfaces. We also look at who is producing the tools, who is implementing them and who is creating and accessing the large amounts of data that is needed to develop prediction machines. One could say, with the wink of an eye, that we are collaborators of this new tribe of human-robot hybrids.