Refik Anadol

Machine Hallucination
Refik Anadol’s most recent synesthetic reality experiments deeply engage with these centuries-old questions and attempt at revealing new connections between visual narrative, archival instinct and collective consciousness. The project focuses on latent cinematic experiences derived from representations of urban memories as they are re-imagined by machine intelligence. For Artechouse’s New York location, Anadol presents a data universe of New York City in 1025 latent dimensions that he creates by deploying machine learning algorithms on over 100 million photographic memories of New York City found publicly in social networks. Machine Hallucination thus generates a novel form of synesthetic storytelling through its multilayered manipulation of a vast visual archive beyond the conventional limits of the camera and the existing cinematographic techniques. The resulting artwork is a 30-minute experimental cinema, presented in 16K resolution, that visualizes the story of New York through the city’s collective memories that constitute its deeply-hidden consciousness.

Thom Kubli

Brazil Now
BRAZIL NOW is a composition that addresses increasing militarization and surveillance within urban areas. Its geographical and acoustic reference is São Paulo, the largest megacity in Latin America. The piece is based on field recordings that capture the symptoms of a Latin American variant of turbo-capitalism with its distinctive acoustic features. Eruptive public demonstrations on the streets are often accompanied by loud, carnivalesque elements. These are controlled by a militarized infrastructure, openly demonstrating a readiness to deploy violence. The sonic documents are analyzed by machine learning algorithms searching for acoustic memes, textures, and rhythms that could be symptomatic for predominant social forces. The algorithmic results are then used as a base for a score and its interpretation through a musical ensemble. The piece drafts a phantasmatic auditory landscape built on the algorithmic evaluation of urban conflict zones.

Refik Anadol

Quantum memories
Quantum Memories is Refik Anadol Studio’s epic scale investigation of the intersection between Google AI Quantum Supremacy experiments, machine learning, and aesthetics of probability. Technological and digital advancements of the past century could as well be defined by the humanity’s eagerness to make machines go to places that humans could not go, including the spaces inside our minds and the non-spaces of our un- or sub-conscious acts. Quantum Memories utilizes the most cutting-edge, Google AI’s publicly available quantum computation research data and algorithms to explore the possibility of a parallel world by processing approximately 200 million nature and landscape images through artificial intelligence. These algorithms allow us to speculate alternative modalities inside the most sophisticated computer available, and create new quantum noise-generated datasets as building blocks of these modalities. The 3D visual piece is accompanied by an audio experience that is also based on quantum noise–generated data, offering an immersive experience that further challenges the notion of mutual exclusivity. The project is both inspired by and a speculation of the Many-Worlds Interpretation in quantum physics – a theory that holds that there are many parallel worlds that exist at the same space and time as our own.

Refik Anadol

WDCH Dreams
The Los Angeles Philharmonic collaborated with media artist Refik Anadol to celebrate our history and explore our future. Using machine learning algorithms, Anadol and his team has developed a unique machine intelligence approach to the LA Phil digital archives – 45 terabytes of data. The results are stunning visualizations for WDCH Dreams, a project that was both a week-long public art installation projected onto the building’s exterior skin (Sept 28 – Oct 6, 2018) and a season-long immersive exhibition inside the building, in the Ira Gershwin Gallery.


System Aesthetics
The works in this series are part of an extensive research project by FIELD, exploring the most relevant machine learning algorithms in code-based illustrations […] We have started a deeper exploration of the less accessible information that is out there, such as scientific papers and open source code publications, to develop an understanding of these algorithms’ inner workings, and translate it into visual metaphors that can contribute to a public debate.