The Feature Extraction Assembly included a series of talks, demos, workshops, and discussion groups that brought together artists and researchers engaged with machine-learning. Under the banner of “artificial intelligence,” machine learning has become central to interlocking domains of political-economic control, including: predictive policing, financialization, ad-tech, social media, and logistics. Unlike the proprietary algorithms used in these applications, artistic uses of machine learning allow people to experience and engage with algorithms directly. In this series, we paired artistic uses of machine learning with scholarly research to explore the social repercussions of algorithmic governance under algorithmic capitalism.
Documentation and Syllabus hosted on Aren.na
Automated Futures is a sensory ethnographic documentary film that illustrates two eras of American economic history by juxtaposing a specialty fiber-optic cable used for high-frequency trading against the decaying infrastructure of the once industrial Rust Belt, emphasizing an eerily parallel detachment from human lives in both of these planetary-scale built environments. The film documents 827 miles of Spread Networks’ flagship dark fiber line through the now post-industrial towns of La Porte, Elkhart, Toledo, Cleveland, Mesopotamia, and Manahoy City. Based on my’s thesis research on the materiality of financial infrastructure, the documentary addresses the operative tension between human agency and technological interdependence within the cultural context of American Independence Day celebrations. Video and audio recordings from the summer of 2013 serve to archive the paradigmatic disjunction between the interests of high finance and the decaying industrial economy, while the structure and soundtrack of the film conspire to question the role of history in the temporal scale of exchange.
Billed by the media as a drama between man versus machine, Deep Blue’s victory over chess champion Garry Kasparav in 1997 was interpreted by many as evidence of a future world dominated by artificial intelligence. In hindsight, Deep Blue’s victory was not a sign of AI’s power over man; it emblematized a particular political-economic moment concerning how firms developed and monetized computing technology. How then should we interpret the political and economic consequences of Google’s victories in games such as Go, Shogi, and Chess? Just as IBM’s Deep Blue stunned the world when it first beat Kasparov, Google’s machine learning software stunned the communities of many competitive game players, first with AlphaGo’s defeat of human Go world-champion Lee Sedol, and subsequently with Alpha Zero’s defeat of the highest ranked open-source chess engine Stockfish—a chess algorithm orders of magnitude better than any human chess player. Like Deep Blue’s victory over Kasperov, Alpha Zero’s victories stand for more than one event in a narrative between man and machine, they signal important changes in the political-economic organization of information technology.
The most obvious difference between Alpha Zero and previous chess engines is apparent in their respective styles of play. Though Deep Blue is now obsolete, the highest ranked open-source chess algorithm named Stockfish incorporates the fundamental design choices built into Deep Blue. Owing to the combinatorial complexity of Chess pieces and chess positions, it is impossible to search through every possible path to a winning configuration. A chess-playing algorithm must be designed to weigh certain moves higher than others. Deep Blue’s and Stockfish’s evaluative system privileges arbitraging material compensation, piece for piece, over positional configurations. Whereas, Alpha Zero’s playing style privileges rapid development and positional advantage over the material value of pieces. Often, Alpha Zero will sacrifice material in the opening to create enough space for the rapid movement of its most dynamic pieces. More surprisingly, Alpha Zero will use its positional advantage to trap its opponents’ pieces in what chess analysts term a “zugzwang”. A zugzwang is a situation in which the obligation to make a move in one’s turn is a serious, often decisive, disadvantage. The most memorable zugzwang game in human chess—known as the Immortal Zugzwang— was played by Friedrich Sämisch and Aron Nimzowitsch in 1923. An immortal zugzwang is so named not because it lasts forever, but because it forecloses on every possibility such that movement becomes impossible without being accompanied by defeat. In one of Google Alpha Zero’s many victories against Stockfish the machine algorithm orchestrated a similar ‘immortal zugzwang’ against Stockfish, forcing the chess engine to resign