Ludovico Rella

Artificial Intelligence algorithms are increasingly deployed outside datacenters and at the so-called Edge, i.e., on end devices such as self-driving cars, smartphones, sensors, and drones, as well as on-premises servers in hospitals and other large but localized institutions. This matters, because, this paper argues, the materiality of the devices on which AI algorithms are deployed influences the “cognition” and behavior of those algorithms. The role of AI’s materiality has thus far not received the attention it deserves, and critical AI literature tends to consider AI algorithms as unproblematically individualized and spatially bounded entities, whereas an attention to materiality also shows how AI’s cognition is always decentralized and spatially distributed. One class of Edge AI algorithms is Federated Learning, where end devices learn partial models independently and sometimes collaborate to produce global models for inference and decision-making. This paper asks what forms of material and spatial cognitive configurations enable specific types of AI agency, and with what ethico-political consequences? Through close reading of ArXiv papers and patents, analyzed, combining Gilbert Simondon and N Katherine Hayles, showing how Federated Learning and Edge AI problematize existing understandings of AI algorithms as immaterial, centralized, and unified subjects. By showing how Edge AI and Federated Learning proceed by compressing models, orchestrating cognition across space, and federating models to produce global inferences, this paper shows that the materiality of the microchips and the spatiality of the data and feature spaces that Edge devices inhabit produce a specific, distributed, and contested form of algorithmic individuality . While distributed Machine Learning algorithms enable more diffuse and granular forms of surveillance and control, they also open the potential to inject partial and situated knowledge into Machine Learning, as an antidote to the current hubris.

Cryptocurrencies, such as Bitcoin, have garnered significant attention in scholarship and beyond. Geographical work on cryptocurrencies has focussed on how their energy demand interacts with local communities and economies. Less is said about the organization of cryptoasset firms and their associated demands. This paper illuminates the complex geographies of one such firm, Bitfury Group, to investigate the global and national forms and structures such companies take and the factors encouraging them to concentrate operations in certain areas. To investigate the latter, we adopt the case study of Bitfury’s operations in Georgia, a South-Caucasian country where its presence is significant. We adapt Haberly et al.’s analytical framework to explore Bitfury’s geographical dimensions. We highlight how cheap electricity, regulatory and taxation regimes, personal encounters and personalities, and the materialities of hardware and energy-saving technology define these geographies and illuminate how Bitfury actively curates advantageous regulatory spaces. We encourage future work exploring Blockchain and Bitcoin technologies to understand the companies involved as simultaneously material and virtual, and as centrepieces in global networks interweaving production and finance.

This paper investigates the role of the materiality of computation in two domains: blockchain technologies and artificial intelligence (AI). Although historically designed as parallel computing accelerators for image rendering and videogames, graphics processing units (GPUs) have been instrumental in the explosion of both cryptoasset mining and machine learning models. The political economy associated with video games and Bitcoin and Ethereum mining provided a staggering growth in performance and energy efficiency and this, in turn, fostered a change in the epistemological understanding of AI: from rules-based or symbolic AI towards the matrix multiplications underpinning connectionism, machine learning and neural nets. Combining a material political economy of markets with a material epistemology of science, the article shows that there is no clear-cut division between software and hardware, between instructions and tools, and between frameworks of thought and the material and economic conditions of possibility of thought itself. As the microchip shortage and the growing geopolitical relevance of the hardware and semiconductor supply chain come to the fore, the paper invites social scientists to engage more closely with the materialities and hardware architectures of ‘virtual’ algorithms and software.

Computer science tends to foreclose the reading of its texts by social science and humanities scholars – via code and scale, mathematics, black box opacities, secret or proprietary models. Yet, when computer science papers are read in order to better understand what machine learning means for societies, a form of reading is brought to bear that is not primarily about excavating the hidden meaning of a text or exposing underlying truths about science. Not strictly reading to make sense or to discern definitive meaning of computer science texts, reading is an engagement with the sense-making and meaning-making that takes place. We propose a strategy for reading computer science that is attentive to the act of reading itself, that stays close to the difficulty involved in all forms of reading, and that works with the text as already properly belonging to the ethico-politics that this difficulty engenders. Addressing a series of three “reading problems” – genre, readability, and meaning – we discuss machine learning textbooks and papers as sites where today's algorithmic models are actively giving accounts of their paradigmatic worldview. Much more than matters of technical definition or proof of concept, texts are sites where concepts are forged and contested. In our times, when the political application of AI and machine learning is so commonly geared to settle or predict difficult societal problems in advance, a reading strategy must open the gaps and difficulties of that which cannot be settled or resolved.

The COVID-19 pandemic dramatically transformed the fundamentals of city management and everyday life. Density has been at the centre of this transformation. But how were densities managed during the pandemic? What are the political implications? And how did people come to perceive and experience densities? Drawing on research in five British cities – Birmingham, Edinburgh, Liverpool, Manchester, and Newcastle – we argue that the pandemic produced a set of new problematisations of density. Those problematisations brought multiple concerns into connection with density: control and rights, the politics of crowds and protest, differential susceptibility to infection, changing orientations to the urban future, and patterns of social anxiety, trust and blame. We seek to advance research in Geography and Urban Studies on how urban densities are governed and experienced, on the urban dimensions of COVID-19, and on how an attention to density generates insight into the social and political life of cities.