How can a deeper understanding of feeling and thinking help us define our ideal relationships with future machines?
/ 3. April - 28. April 2017
/ up to four weeks, full-time in Belgrade, Serbia
/ 10-15 participants accepted
We're thrilled to invite applications for our first creative research residency. It's a program in three parts: self-initiated learning, group teaching and sharing, and community outreach set to take place this spring in Belgrade, Serbia.
Through participation in this program, attendees will have a unique opportunity to investigate topics related to creative artificial intelligence and machine learning for artists with a group of like-minded peers and experienced mentors for up to four-weeks, this April 2017.
Individually, and as a group, we'll spend time examining the ethical and sociocultural dimensions of AI and machine learning, while discussing issues of significance surrounding the ever-increasing integration of these thinking machines into our daily lives. We'll also explore the implications of perception and the notion and possibilities of future feeling machines.
This program will coincide with Belgrade's beloved annual Resonate festival dedicated to creative technology, audiovisual and music performance.
Orchestra music created from human insights and artificial intelligence technology. How? First, Hannah Davis, a creative technologist and musician, used her TransProse artificial intelligence program to aggregate and analyze articles on business and tech.
Overview of Machine Learning
Machine learning is a branch of artificial intelligence concerned with the design of data-driven programs which autonomously demonstrate intelligent behavior in a variety of domains.
Machine learning systems are all around us. When you deposit a check, scan your fingerprint, or post a picture on social media, autonomous algorithms are deployed on the spot to sift through and make sense of your constant interactions with our technology.
Machine learning silently underpins the fabric of our digital infrastructure, discriminating spam e-mail and banking fraud, making light-speed transactions in the global financial market, recommending music and films for customers to buy, deciding what search results are relevant to your queries, and countless more of the daily interactions with electronic media that we take for granted.
Machine learning is the backbone that powers self-driving cars, content recommendation in social media, face identification in digital forensics, and countless other high-level tasks. It has gained rapid interest from the digital arts community, with the recent appearance of numerous artistic hacks of scientific research, such as Deepdream, Stylenet, NeuralTalk, and others.
Creative re-appropriation of these techniques is necessary to refocus machine learning's influence on those things which we care about. Artistic metaphors help clarify that which is otherwise shrouded by layers of academic jargon, making these highly specialized subjects more accessible to everyday people. Taking such an approach, we can repurpose these academic tools and harness their capabilities for creative expression and empowerment.
Gene Kogan's style transfer; a reanimation of the tea party & riddle scene from Alice in Wonderland (1951), restyled by 17 paintings.
Who is this residency for?
This creative research residency is aimed at people working in creative disciplines who wish to learn more about machine intelligence and how to apply it in their own fields. It is *not* aimed at scientists or engineers who are seeking a rigorous technical course on machine learning-- and in fact it is not a *course* at all. Rather, this as a hands-on, self-directed learning retreat.
No specialized knowledge of mathematics or computer science is assumed or expected of participants. We'll build up our own understanding of the subject from elementary building blocks, imagination, analogy, and metaphor.
Examples of style transfer.
This residency is more practical than it is theoretical; we are interested less in proving theorems and equations, and more into bringing people together who are keen on researching relevant topics, exploring various uses of text and data, and/or hacking existing tools for making machines that do interesting things. Of course, we're also really excited to meet new fun, interesting people while we explore machine learning and culture in Belgrade together.
People of diverse backgrounds and interests will all find something to take away from this creative residency experience. If you are a journalist interested in the socioeconomic ramifications of increased automation, a musician wanting to manipulate your instruments with data streams, a designer wishing to imbue your craft with machine artifacts, or you’re just plain old fascinated by the age-old philosophical dilemma of cognition, this residency program is for you.
Doodle Tunes is a software application, built with openFrameworks, that uses computer vision (OpenCV) and convolutional neural networks (ofxCcv) to turn doodles (drawings) of musical instruments into actual music.
€100/wk. (special fee for serbian participants)
€300/mo.* (special fee for serbian participants)
€150/wk. (artists, students, freelancers)
€450/mo.* (artists, students,freelancers)
*Participants welcome to stay from one up to four weeks. Preference given to those who can stay the entire month. If you'd like us to seek out your accommodation for the month, please add €425 to the above fee. For those staying less than a month, please arrange your own accommodations.
Women and persons from LGBTQ+ and other under-represented communities in the tech field highly encouraged to apply!
Gene Kogan / genekogan.com
Gene Kogan is an artist and programmer who is interested in generative systems and the application of emerging technology into artistic and expressive contexts. He writes code for live music, performance, and visual art. He contributes to open-source software projects and gives workshops and demonstrations on topics related to code and art.
He is a contributor to openFrameworks, Processing, and p5.js, an adjunct professor at Bennington College and ITP-NYU, and a former resident at Eyebeam.
Hannah Davis/ hannahishere.com
Hannah Davis is a programmer, data scientist, and musician based in New York, though currently trying out the work-nomad thing. Her work generally falls along the lines of music generation, data visualization, sonification, representation, analysis, natural language processing, machine learning, and storytelling in various formats. She's interested in pretty much everything, but her current passions include music, possible futures, weird, ”subjective” datasets, ideas along the lines of consciousness, language, empathy, identity, and microbiomes.
Hannah is currently working on TransProse, a literature-to-music translation program, and more recently have been starting to create interesting datasets for art and machine learning.