Alexander Reben

Alexander Reben (born 1985) is an American artist, researcher and roboticist.He is best known for his artworks created in collaboration with artificial intelligence, and his research in robotics. Reben's work has been exhibited widely in the United States and Western Europe, including the Museum of Applied Arts, Vienna and the Charlie James Gallery. His work is included in permanent collection of the MIT Museum. He currently serves as Director of Technology and Research at Stochastic Labs, a Berkeley, California-based nonprofit incubator for artists, scientists and engineers. Quotes A GAN stands for Generative Adversarial Network and is a machine learning technique for generation of content. It works by using two systems, one which tries to make convincing output by learning from a dataset and another which judges if the output is real or not. The generator keeps adapting to try to fool the tester and eventually it makes output that is fairly convincing. How would you describe GANs for dummies? GANs are interesting because the output, while in the same style as the input, is almost always surprising and interesting. What is it that you enjoy about working with GAN technique as an artist? People tend to give generated content more credit than they should as far as it’s “intelligence” goes. It is natural for people to want to anthropomorphize technology. What are the misconceptions people have about AI generative art? Indeed, someone asked us: “I’m curious what type of parameters DADAGAN is set to so he doesn’t go too far, doesn’t disobey, nor do anything unwanted cause of an oversight in the programming”. They were quoting Isaac Asimov’s rules for robots, “particularly where they are supposed to obey their master.” What are the misconceptions people have about AI generative art? Right now “AI” is not smart, at all. For very thinly defined problems, it works well, but AGI (Artificial General Intelligence) is quite far away, if it is ever achieved. Also, the system is not a robot, there is no physical component, there is no way for it to interact with or modify the world. How has our view of robots evolved? The DADA dataset was very diverse, which can be both a good and bad thing. It’s good because there is a lot of variety to learn from, and it’s bad because it is hard to “converge” on. So, the output that the model generates is quite abstract, however, this aligns with the spirit of DADA. How do you feel about this particular DADA dataset and how is it different from others? Still, some of the output veers into the figurative. In a relatively short time, it could draw human faces and some creatures that look like animals. How do you imagine DADAGAN evolving? How do you feel about this particular DADA dataset and how is it different from others? There is more that can be done by cleaning up and sorting through the data set. For instance, it can be trained on drawn faces, or many images of a particular style. How do you feel about this particular DADA dataset and how is it different from others? It’s hard not to antropomorphize DADAGAN, because it’s easy to play around with the idea that it is making art. Where do you draw the line? How do you feel about this particular DADA dataset and how is it different from others? We see many things as alive, it is part of our social and survival instincts. Drawing a line is a philosophical question which comes down to each individual’s interpretation of life and consciousness. How do you feel about this particular DADA dataset and how is it different from others? External link Category: living people Category: engineers from the United States Category:Computer scientists from Israel Category:1985 births ‎