SP: The author and new media professor Lev Manovich wrote in one of his books that when one talks about the great successes of AI in recent years, the examples used are the same tasks that were defined at the beginning of the field many decades earlier: natural speech understanding, automated translation, and recognition of objects in photos. But what he sees differently is that AI today plays an equally important role in our cultural life and behavior, with the processes of aesthetic creation and choices becoming increasingly automated. This development raises questions in the development of culture. Manovich wonders whether such automation leads to a decline in aesthetic diversity over time. Do you agree and if so, what are your thoughts on how computational sorting can offer more varied items and thus diversity?
SL: It appears to be because everyone is using the same automatic tools. We produce the same kind of output with the same kind of cultural input. It is also a broader question of communication itself. Language becomes marginalized. That process is also a political process in a sense.In my own work I make automatic systems that produce aesthetic artifacts that are about how these systems are being made. I think that the question is to produce work in which the system itself is aesthetic.
»A lot of my work is language in some way, it is about the computer to be able to make sense.«
SP: With your project, you also look at the poetic potential, which I found out is closely connected to your interest in natural language processing. What does the power of words mean in relation to your project?
SL: I am not a trained artist or programmer; my background is in literature. For me, the interest is always in language and I find it exciting to work with programming and texts. A lot of my work involves language in some way, or is about the computer’s ability to make sense.
I have done a few projects that are directly related to this. One of them is called Are you ready. It is about language and immigration. I start with a video guide to the United States naturalization process, which offers viewers a series of practice questions for the citizenship test. Over time the video becomes increasingly difficult to understand, garbled. The technical part of this project is making use of understanding the sounds of each word or phonemes. The video slowly sorts them and interchanges them until the original becomes multi-voiced, garbled, and obscure.
SP: Your previous works also involve scandals, crimes, surveillance; and highlighting the role that technology can play in those things. You often give the users and viewers full access to information, providing a transparent view of reality. For example, in your works The Good Life (Enron Simulator) and White Collar Crime Risk Zones. What importance do you see in offering this model of transparency?
SL: The Good Life (Enron Simulator) is a project I collaborated on with Tega Brain. It hilariously emerged from another project I did, called Slow Hot Computer, where I experimented with the idea of a computer going very slowly, so that you can’t really do any work. It was a gesture and an experiment in computational strikes, and what it means to be on strike, or how one could even be on strike. I wanted to do something with emails or self-sabotage. I thought it would be great if you get like thousands of emails a day and you just couldn’t use your email account. So I was looking for an email datasets that I could use to receive thousands of emails a day and I happen to stumble upon the Enron Archive. Working with Tega, we realized it could exist on its own and should be separated from the idea of the Slow Hot Computer. This Enron database is very rich. It is fascinating. It was one of the most successful energy companies in America. At the end of 2001, it was revealed that Enron’s reported financial condition was sustained by institutionalized, systematic, and creatively planned accounting fraud. They were basically lying. It fell apart and went bankrupt. The US government investigated, and as part of the investigation, a lot of the data of the company was released. Part of this data was this archive of more than a million emails. Because it was one of the few available email data sets, a lot of machine learning systems have been trained one way or another with Enron data set, which is fascinating. And as a kind of interesting anecdote, one of the first Siris, before it was called Siri, is trained on Enron. The dataset has this incredible history as a cultural document. The story of United States companies and corruption and of computer science and machine learning.