Tabula Rasa: Mechanism, Intelligence, and the Blank Slate in Computing and Urbanism

The Yale Undergraduate Research Journal Volume 2 Issue 1 Spring 2021 Article 4 2021 Tabula Rasa: Mechanism, Intelligence, and the Blank Slate in Computing and Urbanism Claire Gorman Yale University Follow this and additional works at: https://elischolar.library.yale.edu/yurj Part of the Social and Behavioral Sciences Commons Recommended Citation Gorman, Claire (2021) "Tabula Rasa: Mechanism, Intelligence, and the Blank Slate in Computing and Urbanism," The Yale Undergraduate Research Journal: Vol. 2 : Iss. 1 , Article 4. Available at: https://elischolar.library.yale.edu/yurj/vol2/iss1/4 This Article is brought to you for free and open access by EliScholar – A Digital Platform for Scholarly Publishing at Yale. It has been accepted for inclusion in The Yale Undergraduate Research Journal by an authorized editor of EliScholar – A Digital Platform for Scholarly Publishing at Yale. For more information, please contact [email protected]. Tabula Rasa: Mechanism, Intelligence, and the Blank Slate in Computing and Urbanism Cover Page Footnote This project critically examines the “tabula rasa” in computer science and urbanism, questioning the emptiness it describes in landscape through an exploration of its origins in terms of intelligence. Experimentation with tabula rasa in machine learning, where the term describes the originally empty knowledge base of a thinking machine, demonstrates the fallacy of a truly independent artificial intelligence and provides a critical lens through which to interpret the tabula rasa in urbanism. Revisited from this perspective, the case study of Hiroshima, Japan—an iconic example of the urban “blank slate” brought about through total demolition—can be read as a layered complex of historical and cultural components that, like a neural network, resist the separation of information and mechanism. The tabula rasa forms a theoretical conduit across computing and urbanism, enabling a novel transposition of the machine learning framework to cultural landscape analysis. This article is available in The Yale Undergraduate Research Journal: https://elischolar.library.yale.edu/yurj/vol2/iss1/ 4 Gorman | Urban StudiesGorman: Mechanism, Intelligence, and the Blank Slate in Computing and Urbanism Tabula Rasa: Mechanism, Intelligence, and the Blank Slate in Computing and Urbanism By Claire Gorman1 1Program in Computing and the Arts, Yale University ABSTRACT This project critically examines the “tabula rasa” in computer science and urbanism, questioning the emptiness it describes in landscape through an exploration of its origins in terms of intelligence. Experimentation with tabula rasa in machine learning, where the term describes the originally empty knowledge base of a thinking machine, demonstrates the fallacy of a truly independent artificial intelligence and provides a critical lens through which to interpret the tabula rasa in urbanism. Revisited from this perspective, the case study of Hi- roshima, Japan—an iconic example of the urban “blank slate” brought about through total demolition—can be read as a layered complex of historical and cultural components that, like a neural network, resist the separation of information and mechanism. The tabula rasa forms a theoretical conduit across computing and urbanism, enabling a novel transposition of the machine learning framework to cultural landscape analysis. INTRODUCTION is conceptualized as the empty site: it describes unbuilt environ- ments, or wilderness, but also sites that were once built and some- Tabula rasa is a Latin term meaning “blank slate.” It means empty, how razed by human violence or natural disaster. I pursue the traces razed, scorched, evacuated—but it also connotes wild, unbiased, of the erased in the reconstructed, demonstrating through the case and innocent. It means new birthplace and open opportunity, a study of Hiroshima’s Peace Memorial Boulevard that urban sys- dramatized idea of a fresh start. It appears in various creative and tems physically obliterated still exert an enduring influence through scientific disciplines, each of which ascribe to it a new form and the culture of memory and the imperative of resilience. materiality but all of which allude to the fond mythology of a com- pletely new and innocent origin point. My analysis of the tabula rasa in landscape is shaped by the intel- lectual framework of computational intelligence. I address the ur- This paper will investigate the tabula rasa across the fields of com- ban site with attention to the layered transformations of the neural puting and urbanism, using the mechanistic revelations of machine network and interpret changes in the built environment as echoes of learning experiments to drive observations of the tabula rasa in an the mechanistic opacity and iterative clarity of a machine learning urban landscape. It takes a field characterized by programmatic ab- process. I metaphorically apply the procedures and mechanisms of straction and immateriality and holds its approaches up to the light machine learning to this analysis of the urbanist tabula rasa with the of an immediate, immersive, full-scale, and emotionally triggering intention of introducing a new application of computational meth- landscape. It intends to illuminate not only the captivating instabil- ods to the practice of urban studies, exploring a notion of the land- ity of the tabula rasa in each context, but also the powerful potential scape as an intelligent machine. Through this novel critique of the of computational thinking as a framework for parsing the nuances tabula rasa, I reveal the utility of computational thinking for urban of complex urban systems. observation and explore the potential of a mechanistic approach to historical analysis. In computing, the tabula rasa describes the empty structure of an artificial intelligence capable of learning without any innate knowledge. It implies the miraculous production of understanding from a ORIGINS: HISTORY AND THEORY blank and formless mechanism. To demonstrate the artifice of this notion, I discuss a set of experiments that use an unsupervised ma- The tabula rasa denotes emptiness charged with potential. It is a chine learning model—an autoencoder—to interrogate an aspect latent intelligence, a promising void, a machine awaiting its ghost. of inbuilt bias or “knowledge” embedded in the neural network, It has made a long journey through various disciplines and move- demonstrating the insubstantiality of the computational tabula rasa ments in Western thought: the concept originated in philosophy, by exposing the fundamental dependence of machine learning on then wove through various notions of human nature to psychology, innately ingrained information. where its impact on theories of knowledge and learning brought it ultimately to computer science by way of artificial intelligence I translate insights from this experiment to the field of urbanism, (AI). where they inform an analysis of the tabula rasa produced by the atomic bomb in Hiroshima, Japan. Architecturally, the tabula rasa The earliest ancestor of the computational blank slate appears in Published by EliScholar – A Digital Platform for Scholarly Publishing at Yale, 2021 1 22 YURJ | Vol 2.1 Spring 2021 The Yale Undergraduate Research Journal, Vol. 2 [2021], Iss. 1, Art. 4 Gorman | Urban Studies Aristotle’s 350 BC treatise De Anima, where he philosophizes that substrate and again updates the “white paper” to a “notebook” “the mind is in a sense potentially whatever is thinkable, though freshly purchased. Important in Turing’s notion of the tabula rasa is actually it is nothing until it has thought,” (Book I) and compares his distinction of mechanism from information: the empty and in- this immaterial intelligence to the characters anticipated by a “writ- nocent consciousness he describes possesses no innate knowledge written across its pages, but it does require some minor mechanism to bind them together. Then, is the “tabula rasa” here the empty page or the notebook itself? An investigation of that question—of technically locating the computational tabula rasa—must pursue the distinction between information and mechanism. EXPERIMENT To empirically evaluate the computational tabula rasa, I conducted an experiment on my own machine learning model. I built a sim- ple autoencoder to perform the task of reducing and reconstructing images of a handwritten zero and a handwritten one. I built the model properly and trained it successfully so that it was capable of reconstructing each type of image correctly. Then, I interrupted this learning by modifying an aspect of the original model I would clas- sify as “innate knowledge” and demonstrating the effects of these modifications on the network’s ability to learn. The impairments Figure 1: The Aristotelian “writingtablet,” technically blank but antici- brought on by my interventions demonstrate that even unsuper- pating its own inscription. Even naming it subverts this blankness. vised learning relies on a certain quantity of inbuilt information. ing-tablet” upon which nothing is written yet. This statement es- tablishes the enduring cognitive metaphor of a promising empty The substrate of this process is the artificial neural network (neural vessel, awaiting the imminent but foreign intelligence that will in- net), a computing system vaguely inspired by the biological neural habit it. networks that make up the human brain. A neural net is composed The tabula rasa later appears in the seminal “Essay Concerning Hu- man Understanding” written

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