https://catherine-griffiths.com catherine griffiths Wed, 14 Aug 2024 13:00:30 +0000 https://catherine-griffiths.com en https://catherine-griffiths.com/Catherine-Griffiths Sat, 13 Apr 2019 21:12:41 +0000 catherine griffiths https://catherine-griffiths.com/Catherine-Griffiths https://catherine-griffiths.com/NVDA Wed, 14 Aug 2024 13:00:30 +0000 catherine griffiths https://catherine-griffiths.com/NVDA NVDA [THE POST-WORK OF CLIMATE ACTIVISM] <img width="2560" height="1440" width_o="2560" height_o="1440" data-src="https://freight.cargo.site/t/original/i/e46b7aeefbef31d88f3feb6d099308f2eea41d24fed7eb6bb64e135ef739cb41/skeleton-detection-01.jpg" data-mid="216348698" border="0" src="https://freight.cargo.site/w/1000/i/e46b7aeefbef31d88f3feb6d099308f2eea41d24fed7eb6bb64e135ef739cb41/skeleton-detection-01.jpg" /> NVDA [The Post-Work of Climate Activism] is a multi-channel video installation work-in-progress.&nbsp;NVDA is an ongoing design research project exploring the intersection of Non-Violent Direct Action as an activist strategy in response to the threat of climate collapse, increased police powers and new laws to prevent climate activism, and the looming applications of machine learning technologies in urban space. In April 2023, the UK government introduced an ammendment to the Public Order Act, to broadened the legal definition of ‘serious disruption’, giving the police almost unlimited powers to shut down any protest that causes a ‘more than minor disturbance’. This change is known as Section 12. <img width="2560" height="1440" width_o="2560" height_o="1440" data-src="https://freight.cargo.site/t/original/i/9dde1be2d3dfeac7f7878e8f4268b2672cfffa990a7b1ff1c384d5d91af85a82/skeleton-detection-02.jpg" data-mid="216348699" border="0" src="https://freight.cargo.site/w/1000/i/9dde1be2d3dfeac7f7878e8f4268b2672cfffa990a7b1ff1c384d5d91af85a82/skeleton-detection-02.jpg" /> <img width="2560" height="1440" width_o="2560" height_o="1440" data-src="https://freight.cargo.site/t/original/i/89a92da6239caea36a17e5eee689c84c80b7cc9d3431d96e425dda3005b59435/skeleton-detection-03.jpg" data-mid="216348700" border="0" src="https://freight.cargo.site/w/1000/i/89a92da6239caea36a17e5eee689c84c80b7cc9d3431d96e425dda3005b59435/skeleton-detection-03.jpg" /> <img width="2560" height="1440" width_o="2560" height_o="1440" data-src="https://freight.cargo.site/t/original/i/979c690f0977df6bac84dd55b2a63754c1fe29f7c6583a8a9683cb0b33260d54/slow-walking-01.jpg" data-mid="216348701" border="0" src="https://freight.cargo.site/w/1000/i/979c690f0977df6bac84dd55b2a63754c1fe29f7c6583a8a9683cb0b33260d54/slow-walking-01.jpg" /> This project explores an ethnographic approach to document the work of Just Stop Oil, a prominent climate activist group in the UK that demands the end of new oil and gas licenses, juxtaposed with the decline of civil liberties associated with Section 12, and tactics for their potential circumvention. The NVDA project utilizes machine learning techniques to examine the ontologies of policing practices, and as a counter-algorithmic proposition, develops a counter-ontology, emerging from the ethnographic tactics of Just Stop Oil. <img width="2560" height="1440" width_o="2560" height_o="1440" data-src="https://freight.cargo.site/t/original/i/9fb28e4e0e30516e3d4097a8f26cc0276598f05a8612fe4d1ab6929ca1930681/face-detection-02.jpg" data-mid="216348697" border="0" src="https://freight.cargo.site/w/1000/i/9fb28e4e0e30516e3d4097a8f26cc0276598f05a8612fe4d1ab6929ca1930681/face-detection-02.jpg" /> <img width="2560" height="1440" width_o="2560" height_o="1440" data-src="https://freight.cargo.site/t/original/i/72abaddcb179913b059d897b57858ca6dd24ac1c792ad77e35b08a9d7a2a6615/face-detection-01.jpg" data-mid="216348696" border="0" src="https://freight.cargo.site/w/1000/i/72abaddcb179913b059d897b57858ca6dd24ac1c792ad77e35b08a9d7a2a6615/face-detection-01.jpg" /> <img width="2560" height="1440" width_o="2560" height_o="1440" data-src="https://freight.cargo.site/t/original/i/a27e6fd8657998c4123ca90761ab8af8dd0f226181d2f5facd18717fe5ab6869/body-cam-01.jpg" data-mid="216348695" border="0" src="https://freight.cargo.site/w/1000/i/a27e6fd8657998c4123ca90761ab8af8dd0f226181d2f5facd18717fe5ab6869/body-cam-01.jpg" /> https://catherine-griffiths.com/SANDY-FLATTS-LANE Tue, 13 Aug 2024 21:21:41 +0000 catherine griffiths https://catherine-griffiths.com/SANDY-FLATTS-LANE SANDY FLATTS LANE [DOMESTIC LABOUR] <img width="2558" height="720" width_o="2558" height_o="720" data-src="https://freight.cargo.site/t/original/i/a6e9d9b6b03e0f245067e2c711ae5be4515160974c3afd221feadcfa8e1e91b5/edit-2.jpg" data-mid="216321281" border="0" src="https://freight.cargo.site/w/1000/i/a6e9d9b6b03e0f245067e2c711ae5be4515160974c3afd221feadcfa8e1e91b5/edit-2.jpg" /> &nbsp; Sandy Flatts Lane [Domestic Labour] is a multi-channel video installation work-in-progress.&nbsp; Exploring the potential role of machine learning algorithms in a suburban home, reflecting on ideas from xenofeminism and the prospects of AI on gendered labor, labors of care, and reproductive labor, including its liberatory potential. The work takes the form of a personally situated narrative. I filmed my mother in her home and re-enacted her domestic tasks to create a situated motion dataset. Taking cues from other feminist and cinematic portrayals of space: the social documentary film 'No Home Movie' by Chantal Akerman, shot in her mother's apartment, and the paintings of Caroline Walker who focuses on women's work in the home. <img width="2558" height="720" width_o="2558" height_o="720" data-src="https://freight.cargo.site/t/original/i/ea3733eeb7291dd91c9607e2a3c2a3d9bd3dcfdde766dbbd69ae921da3479374/edit-1.jpg" data-mid="216321280" border="0" src="https://freight.cargo.site/w/1000/i/ea3733eeb7291dd91c9607e2a3c2a3d9bd3dcfdde766dbbd69ae921da3479374/edit-1.jpg" /> <img width="1920" height="1080" width_o="1920" height_o="1080" data-src="https://freight.cargo.site/t/original/i/de3fd99220af1fc4ffea603fb454942f56ba9ff090846dd42594b8840c3e8952/green-screen.jpg" data-mid="216321285" border="0" src="https://freight.cargo.site/w/1000/i/de3fd99220af1fc4ffea603fb454942f56ba9ff090846dd42594b8840c3e8952/green-screen.jpg" /> <img width="1920" height="1080" width_o="1920" height_o="1080" data-src="https://freight.cargo.site/t/original/i/8e221c5b83d027a48e27320579c4a61fe77b9b6bcae7382ab82fc307e67cf854/interface-1.jpg" data-mid="216321286" border="0" src="https://freight.cargo.site/w/1000/i/8e221c5b83d027a48e27320579c4a61fe77b9b6bcae7382ab82fc307e67cf854/interface-1.jpg" /> My point of departure is the re-enactment of domestic labor, for which I have used motion capture technology to develop an algorithmic rendition.I imagine ways to politicize repositories of 3D digital assets and datasets that typically avoid such positioning. &nbsp;Thinking about the near future of the home as a space that produces datasets used to train domestic robots and other learning algorithms that are set to enter intimate spaces and care settings - especially those contexts of unpaid and low-paid work. The privacy of the home is a domain increasingly targeted by the looming vectorization of the body and domestic space. We hear of technology originally developed for prison surveillance, gradually penetrating the home and workplace. The project situates a discussion on the future of technology and how we want to live with it in spaces associated with deep intimacy, privacy, and anomie. <img width="1279" height="719" width_o="1279" height_o="719" data-src="https://freight.cargo.site/t/original/i/65bc200ae49ea223809073b6a5478cd7337af593d949db288aeb3f19ac619307/edit-4.jpg" data-mid="216321694" border="0" src="https://freight.cargo.site/w/1000/i/65bc200ae49ea223809073b6a5478cd7337af593d949db288aeb3f19ac619307/edit-4.jpg" /> <img width="1920" height="1080" width_o="1920" height_o="1080" data-src="https://freight.cargo.site/t/original/i/3f30c840748a5eb302a5cecea0189f60f481cf1714443ec892dbb5eb434c7c96/interface-2.jpg" data-mid="216321287" border="0" src="https://freight.cargo.site/w/1000/i/3f30c840748a5eb302a5cecea0189f60f481cf1714443ec892dbb5eb434c7c96/interface-2.jpg" /> https://catherine-griffiths.com/LABOR-OPTICS Sun, 27 Nov 2022 17:10:02 +0000 catherine griffiths https://catherine-griffiths.com/LABOR-OPTICS LABOR OPTICS &nbsp; Labor Optics is part of an ongoing research into machine learning technologies, labor ethics, and the futures of work. The visualizations that form part of the this project reconsider Worker Activity Recognition algorithms and their potential implications on worker rights and automated labor relations. Worker Activity Recognition algorithms are trained to recognize the micro movements of the human body in intricate detail and predict their behaviours. Here, you can see people at work on a construction site and the software analyzing their activities, ultimately with a view to evaluate their productivity. <img width="3595" height="2008" width_o="3595" height_o="2008" data-src="https://freight.cargo.site/t/original/i/65beb2671bb91248fdd63462b850b32b78da9ea3ca7211a97ad2c28dd7c61dce/Labor_Flows_01.jpg" data-mid="161718296" border="0" src="https://freight.cargo.site/w/1000/i/65beb2671bb91248fdd63462b850b32b78da9ea3ca7211a97ad2c28dd7c61dce/Labor_Flows_01.jpg" /> <img width="3595" height="2008" width_o="3595" height_o="2008" data-src="https://freight.cargo.site/t/original/i/39e48769140c774c835ee47f754b68349222baee577f80b1227629ccd312e0a0/Labor_Flows_02.jpg" data-mid="161718300" border="0" src="https://freight.cargo.site/w/1000/i/39e48769140c774c835ee47f754b68349222baee577f80b1227629ccd312e0a0/Labor_Flows_02.jpg" /> Intersecting this work, in the US recently, The White House published a Blueprint for an AI Bill of Rights, and pertinent to this research is that it begins to recognize and outline how ‘work’ is a sensitive domain when it comes to AI that can limit people’s rights. I am interested in looking at Worker Activity Recognition prototypes as something that are not yet fully implemented in the world, in real workplaces, so their form has yet to be finalized. There is still space to question its ontology, critique its code, shape its ethics, and reconsider what the futures of work looks like.&nbsp; Is this how we want to experience being a worker in the world?&nbsp; <img width="3595" height="2008" width_o="3595" height_o="2008" data-src="https://freight.cargo.site/t/original/i/c360644d35c356b0e5443796feb087cd52dee3c7e35098bb9109edfb30f80ccf/LABOR_FLOWS_03.jpg" data-mid="161718301" border="0" src="https://freight.cargo.site/w/1000/i/c360644d35c356b0e5443796feb087cd52dee3c7e35098bb9109edfb30f80ccf/LABOR_FLOWS_03.jpg" /> <img width="3595" height="2008" width_o="3595" height_o="2008" data-src="https://freight.cargo.site/t/original/i/c6db7880a00382786ddd3d95ff1b6d1fab77f821d2a8740d1ad85a132f0e3419/LABOR_FLOWS_04.jpg" data-mid="161718305" border="0" src="https://freight.cargo.site/w/1000/i/c6db7880a00382786ddd3d95ff1b6d1fab77f821d2a8740d1ad85a132f0e3419/LABOR_FLOWS_04.jpg" /> From the perspective of urbanism and architecture, it is notable that the testing ground for such prototypes appears to be the built environment, constructions sites, and other industrial workplaces. For me, these images taken from within the neural network, suggest an aesthetics of this power asymmetry, an aesthetics of the ethical contention at the heart of worker activity recognition. Contextually, there are several points that frame how to think about this new technology. There is the post-covid context of work, in which working remotely, and consequently managing workers remotely, has been normalized so quickly that we haven’t had time to think through its implications. There are already digital infrastructure platforms such as Uber that have shifted the ontology of workers and negated workers’ employment rights. In Amazon Warehouses we read about how people work in service of robotics, and the needs to the robotic system are prioritized over the needs of the human body. <img width="3595" height="2008" width_o="3595" height_o="2008" data-src="https://freight.cargo.site/t/original/i/2c39ab7c3b19dbc91824de5503e693b47e3560e83088e0709016889c00abbf0b/LABOR_FLOWS_05.jpg" data-mid="161718311" border="0" src="https://freight.cargo.site/w/1000/i/2c39ab7c3b19dbc91824de5503e693b47e3560e83088e0709016889c00abbf0b/LABOR_FLOWS_05.jpg" /> <img width="3600" height="1001" width_o="3600" height_o="1001" data-src="https://freight.cargo.site/t/original/i/c3e328186c9df0ce746968dd98204fff82e53f6bbff97e4830f128aa8a36abba/LABOR_FLOWS_07.jpg" data-mid="161718325" border="0" src="https://freight.cargo.site/w/1000/i/c3e328186c9df0ce746968dd98204fff82e53f6bbff97e4830f128aa8a36abba/LABOR_FLOWS_07.jpg" /> Is this what we desire for the future of work? Is this the promise of “Industry 4.0”? What does this disembodied managerial agency mean for worker’s real bodies? Is bypassing traditional human management a mask for the removal of rights, by negating any means of redress and explanation? Do we risk reneging on fair labour rights fought for during the C20th? There is the mental exhaustion of being aware that you are constantly being surveilled to such a level of detail. It is documented that the use of machine learning in low rights workplaces is associated with wage theft. Far from the promise of automation that liberates the human body from the arduousness of physical labor; instead, we are seeing the robotization of the human body. What is the landscape of labour rights and relations - 4.0 - to meet the landscape of machine learning in the workplace? <img width="3595" height="2008" width_o="3595" height_o="2008" data-src="https://freight.cargo.site/t/original/i/0f354f8ee61ba89efcc2ced915a3805c88bbba72f777b1310c9499d4f7e3fef0/LABOR_FLOWS_06.jpg" data-mid="161718313" border="0" src="https://freight.cargo.site/w/1000/i/0f354f8ee61ba89efcc2ced915a3805c88bbba72f777b1310c9499d4f7e3fef0/LABOR_FLOWS_06.jpg" /> https://catherine-griffiths.com/LABOR-DOMAINS Sun, 27 Nov 2022 00:25:49 +0000 catherine griffiths https://catherine-griffiths.com/LABOR-DOMAINS LABOR DOMAINS &nbsp; Labor Domains thinks through the implications of machine learning and its impact on labour ethics, workers, and the futures of work. From these intersections I have developed the concept of reflexive software development, as apractice-based research that seeks to contest the normative logics of machine learning algorithms and think through new counteralgorithmic imaginaries. <img width="3603" height="2022" width_o="3603" height_o="2022" data-src="https://freight.cargo.site/t/original/i/9cfbb5520bd22892d17e505c86f711ef4f584ad935faee142d7d13274998252b/Agricultural_Workers.jpg" data-mid="160247237" border="0" src="https://freight.cargo.site/w/1000/i/9cfbb5520bd22892d17e505c86f711ef4f584ad935faee142d7d13274998252b/Agricultural_Workers.jpg" /> Reflexive software is a means of visually and critically unfolding complex intersecting sociotechnical computation systems. Reflexive software development holds algorithms and their ideological positionality in contestation. In contrast to traditional software development that is instrumental, producing new functional tools to achieve a defined task, reflexive software development critically reflects on its own processes, on its own code. It is a form of software development that thinks about the development of software, that engages an awareness of the social and ideological circumstances of its own production. <img width="2032" height="1220" width_o="2032" height_o="1220" data-src="https://freight.cargo.site/t/original/i/f23339210b0ee345ddb36bbfb42d041f6cb688e511466d57284dcff49eaa1a5e/Screenshot-2022-01-25-at-17.51.27.png" data-mid="160247240" border="0" src="https://freight.cargo.site/w/1000/i/f23339210b0ee345ddb36bbfb42d041f6cb688e511466d57284dcff49eaa1a5e/Screenshot-2022-01-25-at-17.51.27.png" /> Reflexive software is a method that reframes a key research question in machine learning, which is the ‘interpretability problem’. Instead of asking ‘how to solve the problem of interpretability’, I ask ‘how to contest the problem of interpretability’ in order to reframe the value system that drives its development. <img width="2032" height="1220" width_o="2032" height_o="1220" data-src="https://freight.cargo.site/t/original/i/b98a8d19e6bce47b33927d0068794a97c7a0a53db636f6b36ffd7d9ea4f36d23/Screenshot-2022-01-25-at-17.55.28.png" data-mid="160247241" border="0" src="https://freight.cargo.site/w/1000/i/b98a8d19e6bce47b33927d0068794a97c7a0a53db636f6b36ffd7d9ea4f36d23/Screenshot-2022-01-25-at-17.55.28.png" /> <img width="2032" height="1220" width_o="2032" height_o="1220" data-src="https://freight.cargo.site/t/original/i/730e11d4e3c6b114f64c90f2997e8b5416783d0286a0a47a57c3cccd9b0dff0a/Screenshot-2022-01-25-at-17.56.22.png" data-mid="160247242" border="0" src="https://freight.cargo.site/w/1000/i/730e11d4e3c6b114f64c90f2997e8b5416783d0286a0a47a57c3cccd9b0dff0a/Screenshot-2022-01-25-at-17.56.22.png" /> Reflexive software seeks to break the fourth wall of computation. It does not necessarily prioritize a seamless and simple user experience; instead, it attempts to design critical elements of deconstruction, revelation, discontinuity, and contestation into the user experience in order for an audience, perhaps a non-technical audience to engage questions about the new era of machine learning. A typical approach to machine learning software situates the user in an unconscious, immersive narrative of the innovation of pure automation and subservience to its logic. In fact, every algorithm and training data set is a mechanical turk that disguises human subjects, human labor, institutional design approaches, and an ideological system behind its development. Reflexive software attempts to offer the user a sense that our algorithmic infrastructures are contestable and could be designed from alternative imaginaries. <img width="1988" height="1176" width_o="1988" height_o="1176" data-src="https://freight.cargo.site/t/original/i/f294a741bc1a527d1bc85a2fb6e8ef5d9dbb3050667535b1dbc5ede30d5d5818/Screenshot-2022-01-25-at-17.44.12.png" data-mid="160247238" border="0" src="https://freight.cargo.site/w/1000/i/f294a741bc1a527d1bc85a2fb6e8ef5d9dbb3050667535b1dbc5ede30d5d5818/Screenshot-2022-01-25-at-17.44.12.png" /> <img width="2032" height="1220" width_o="2032" height_o="1220" data-src="https://freight.cargo.site/t/original/i/3de58c464f6ba5eacb1e91b3dc4eb78921d2103c58174cf5442c11da03a48b2d/Screenshot-2022-01-25-at-17.47.55.png" data-mid="160247239" border="0" src="https://freight.cargo.site/w/1000/i/3de58c464f6ba5eacb1e91b3dc4eb78921d2103c58174cf5442c11da03a48b2d/Screenshot-2022-01-25-at-17.47.55.png" /> Two other input videos that the software operates on to think through machine learning’s impact on Textile Workers and Warehouse Workers. <img width="3603" height="2022" width_o="3603" height_o="2022" data-src="https://freight.cargo.site/t/original/i/cb53146c92cdab5cff750b7712040a8032dcd29d31047b286cadb44d76ed7919/Textile_Workers.jpg" data-mid="160247562" border="0" src="https://freight.cargo.site/w/1000/i/cb53146c92cdab5cff750b7712040a8032dcd29d31047b286cadb44d76ed7919/Textile_Workers.jpg" /> <img width="3603" height="2022" width_o="3603" height_o="2022" data-src="https://freight.cargo.site/t/original/i/980199aa726d9b860d4bcbe49496def6153fb3af0d683bf6560e7ea29a787914/Warehouse_Workers.jpg" data-mid="160247563" border="0" src="https://freight.cargo.site/w/1000/i/980199aa726d9b860d4bcbe49496def6153fb3af0d683bf6560e7ea29a787914/Warehouse_Workers.jpg" /> https://catherine-griffiths.com/IRISES Thu, 13 Oct 2022 15:52:14 +0000 catherine griffiths https://catherine-griffiths.com/IRISES IRISES &nbsp;An excerpt from the artwork Irises, a digital video and interactive simulation that explores a more sensory dynamics of simple machine learning classification with painterly aesthetics. https://catherine-griffiths.com/VISUALIZING-ALGORITHMS Sat, 13 Apr 2019 21:12:42 +0000 catherine griffiths https://catherine-griffiths.com/VISUALIZING-ALGORITHMS VISUALIZING ALGORITHMS &nbsp; Visualizing Algorithms is a research project exploring tactics to engage with decision-making in simple machine learning systems. The project studies how a decision tree classifier works allowing a user to interactively manipulate and frame the view of a decision-making process that usually occurs beyond human perception. By slowing down the speed of the computation to a human-scale and allowing a user to navigate and zoom into the data that populates the network, the algorithm’s landscape and cognition is accessible. A decision tree is a model for predicting how data should be classified based on a previous training process. The objective of visualization techniques such as this one, provide broader access, a form of scrutiny, and process of accountability, with algorithmic decision-making. The user can identify individual data points as they flow through the algorithms network of internal paths and decisions, reverse-engineering the process. The project primarily works with synthetic data to speculate on the concept of bias augmentation. Whilst bias in algorithms is usually attributed to the original data set, being a representation of a bias in society, bias augmentation enquires into whether a small bias in input data can be amplified through the algorithmic structure and iterative process. The project proposes that the increasingly wider adoption of autonomous decision-making system needs to be accompanied by new visual design tools for intuitive insight that do not require technical knowledge, allowing the public to hold accountable processes that might unintentionally or intentionally accentuate bias. Research questions: - can the visualization of algorithms be used as an a-linguistic tool to re-engage with decision-making in prediction systems, where we are at risk of losing our connection of decision-making? - can interaction design, generative design, and critical code studies, combine as an effective method to visualize ethical positions in algorithms, including bias, mistakes, and interpretability? - to consider bias augmentation, what can be learnt by temporarily isolating the meaning in data, to focus on the effect that structure and process play in the generation of bias? - what does it mean to learn, in artificial intelligence systems, and is the anthropomorphic analogy productive? <img width="1926" height="1109" width_o="1926" height_o="1109" data-src="https://freight.cargo.site/t/original/i/a2dbc588980b3a96f1428810005a4e5fca108e0e699fbb20313edd1ec0988deb/5Capture.PNG" data-mid="40010175" border="0" data-scale="78" src="https://freight.cargo.site/w/1000/i/a2dbc588980b3a96f1428810005a4e5fca108e0e699fbb20313edd1ec0988deb/5Capture.PNG" /> Basic visualization of decision tree structure and classified data in interactive application. <img width="1911" height="1078" width_o="1911" height_o="1078" data-src="https://freight.cargo.site/t/original/i/86e3bede8ca7415fec9cc4cf325652680d4944c19cef020984fb1eb290f181f0/N04.jpg" data-mid="40012591" border="0" data-scale="78" src="https://freight.cargo.site/w/1000/i/86e3bede8ca7415fec9cc4cf325652680d4944c19cef020984fb1eb290f181f0/N04.jpg" /> Large decision tree using synthetic data. &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;<img width="1936" height="1119" width_o="1936" height_o="1119" data-src="https://freight.cargo.site/t/original/i/32358978a9bf474b6adf3f1cf13820780c828988f8f77546bd7a4cb57990c8c0/16Capture.PNG" data-mid="40010157" border="0" data-scale="78" src="https://freight.cargo.site/w/1000/i/32358978a9bf474b6adf3f1cf13820780c828988f8f77546bd7a4cb57990c8c0/16Capture.PNG" />Mistakes in classification are highlighted.&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp;<img width="1936" height="1119" width_o="1936" height_o="1119" data-src="https://freight.cargo.site/t/original/i/983465c5c78e28c9b3c271f545ad981bd0b18153f7cb38abed8a65371d9b8502/14Capture.PNG" data-mid="40010155" border="0" data-scale="78" src="https://freight.cargo.site/w/1000/i/983465c5c78e28c9b3c271f545ad981bd0b18153f7cb38abed8a65371d9b8502/14Capture.PNG" /> User can zoom in on indivual data points and view their reverse-engineered pathways. <img width="1911" height="1078" width_o="1911" height_o="1078" data-src="https://freight.cargo.site/t/original/i/763a4c6c2650b714e45cbcf613be00333edcbecba038c36bed916f7f8416893a/N07.jpg" data-mid="40012596" border="0" data-scale="78" src="https://freight.cargo.site/w/1000/i/763a4c6c2650b714e45cbcf613be00333edcbecba038c36bed916f7f8416893a/N07.jpg" />&nbsp; The application maps out the algorithm spatially, to see its full possibility space. &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; <img width="1911" height="1078" width_o="1911" height_o="1078" data-src="https://freight.cargo.site/t/original/i/51c2df571ed79911ae20e95b2478e99244e8a3fe2f6fcd6a01bdeb3232ca735c/N02.jpg" data-mid="40012589" border="0" data-scale="78" src="https://freight.cargo.site/w/1000/i/51c2df571ed79911ae20e95b2478e99244e8a3fe2f6fcd6a01bdeb3232ca735c/N02.jpg" /> Large decision tree of synthetic data. The overall data organization is a combination of a physics simulation, self-organizing tree structure, and user-defined parameters. https://catherine-griffiths.com/AUTOMATA-I Sat, 13 Apr 2019 21:12:43 +0000 catherine griffiths https://catherine-griffiths.com/AUTOMATA-I AUTOMATA I Automata I is research project looking into techniques of machine vision. The project identifies image process analysis as a basis for the computation of autonomous pixel manipulation agents following a cellular automata logic. Image analysis, as used in surveillance techniques, at its core is an analysis of patterns and relationships between pixels. This project brings to light the operations behind image analysis, allowing the viewer to speculate on proactive alterations of image content. The work uses a Langton’s Ant algorithm, which navigates the luminosity field of a satellite image. A large population of agents, with simple movement rules denoted by blue and red circles, navigate the landscape, terraforming and re-composing the terrain. The grid-based calculations behind the cellular automata model define the moving behavior of the ant, allowing for a perpetual feedback loop between the substrate and active agent. The satellite image of a site in the Amazon, the confluence of two rivers, known as The Meeting of The Waters, works analogously. Due to rivers’ differences in water density, temperature, and speed, they do not merge, but run alongside each other in the same channel, demarcated by the different colours. The algorithm generatively repatterns them. The process is presented in two formats; the color output which is the human view, which may hold cultural meaning, and the greyscale output which is the algorithm’s view of the underlying state system and rule set on which the agents perform. The objective of juxtaposing these two modes or interpretations of the image/code is to provide an insight into the kind of calculations performed behind image analysis techniques. The greyscale image, showing the algorithm’s interpretation of the scene. Pixel luminosity drives the behavior of the agents, shown as blue and red circles. <img width="800" height="450" width_o="800" height_o="450" data-src="https://freight.cargo.site/t/original/i/a0a07f4570ec495d1e76f09f6234c6df6ac96f3a7bc988b7bd7da70732d35af6/10.png" data-mid="40010524" border="0" data-scale="100" src="https://freight.cargo.site/w/800/i/a0a07f4570ec495d1e76f09f6234c6df6ac96f3a7bc988b7bd7da70732d35af6/10.png" /> A langton’s ant operates similarly to a cellular automata, except only one cell in the grid changes each frame, functioning like an autonomous agent.<img width="1020" height="677" width_o="1020" height_o="677" data-src="https://freight.cargo.site/t/original/i/9b0da3591dd70de6b10af5ef2cb39d43ea28d5c72dba6f0f4de1fef1d03e38f2/Screen-Shot-2018-08-14-at-4.52.39-PM.png" data-mid="40010521" border="0" src="https://freight.cargo.site/w/1000/i/9b0da3591dd70de6b10af5ef2cb39d43ea28d5c72dba6f0f4de1fef1d03e38f2/Screen-Shot-2018-08-14-at-4.52.39-PM.png" /> The Meeting of The Waters, a site in the Amazon of the confluence of two rivers whose waters do not mix.&nbsp; <img width="2000" height="1124" width_o="2000" height_o="1124" data-src="https://freight.cargo.site/t/original/i/2c366193896d0d9fec08a7c01c75564c4bc64130563b37f741eed7f5f084a333/Screen-Shot-2019-04-13-at-6.00.31-PM_small.png" data-mid="40010757" border="0" data-scale="87" src="https://freight.cargo.site/w/1000/i/2c366193896d0d9fec08a7c01c75564c4bc64130563b37f741eed7f5f084a333/Screen-Shot-2019-04-13-at-6.00.31-PM_small.png" />Pixel manipulation cellular automata logic. https://catherine-griffiths.com/AUTOMATA-II Sat, 13 Apr 2019 22:12:42 +0000 catherine griffiths https://catherine-griffiths.com/AUTOMATA-II AUTOMATA II Automata II continues the research of looking into techniques of machine vision established in Automata I. This iteration looks into an open source computer vision library, OpenCV that is used infacial recognition and surveillance cameras. By applying the same cellular automata logic from Automata I the visualization shows how image filters algorithmically reduce the complexity of an image, creating a different reading of the image to compute on, in this case to detect motion. This defines the technique called background subtraction. In this piece, a recording of a chameleon is used to denote localized and subtle forms of movement, which in turn are captured by an algorithmic process that can only perceive motion. The chameleon’s stillness operates as a mechanism of camouflage, in this case from a computer process that is only able to process the variation of pixels over time. Whilst the first video shows the algorithm’s method of blob-detection for movement, this second pass removes the background and prints only the detected motion, in terms of pixels that have changed between frames, rendering the chameleon’s stillness as a form of algorithmic camouflage, and it motion as revealing itself to the algorithm. &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp; <img width="800" height="450" width_o="800" height_o="450" data-src="https://freight.cargo.site/t/original/i/f3bab9a3b5ffb9b3bfbd307ba3be0ccf341d94ac7d5751ac94444434391ca260/5.png" data-mid="40010827" border="0" src="https://freight.cargo.site/w/800/i/f3bab9a3b5ffb9b3bfbd307ba3be0ccf341d94ac7d5751ac94444434391ca260/5.png" />A visualization of a surveillance camera’s gaze of an urban scene, through OpenCV’s blob detection algorithm. <img width="3840" height="2150" width_o="3840" height_o="2150" data-src="https://freight.cargo.site/t/original/i/7ecf3e699fed130bec95c8b6be372b77690120527e1936848e05d2fa44b187a8/Screen-Shot-2019-04-13-at-6.03.57-PM.png" data-mid="40010828" border="0" data-scale="86" src="https://freight.cargo.site/w/1000/i/7ecf3e699fed130bec95c8b6be372b77690120527e1936848e05d2fa44b187a8/Screen-Shot-2019-04-13-at-6.03.57-PM.png" /> <img width="3838" height="2142" width_o="3838" height_o="2142" data-src="https://freight.cargo.site/t/original/i/91643390e0b66e97678e015fb77f0cee91ae5fca0ebde97c306c98ff1ef29134/Screen-Shot-2019-04-13-at-6.04.46-PM.png" data-mid="40010826" border="0" data-scale="86" src="https://freight.cargo.site/w/1000/i/91643390e0b66e97678e015fb77f0cee91ae5fca0ebde97c306c98ff1ef29134/Screen-Shot-2019-04-13-at-6.04.46-PM.png" /> https://catherine-griffiths.com/CINEMA-FOR-ROBOTS Sat, 13 Apr 2019 21:12:43 +0000 catherine griffiths https://catherine-griffiths.com/CINEMA-FOR-ROBOTS CINEMA FOR ROBOTS Cinema For Robots is an exploration into reflexivity and computational technology. The technique of photogrammetry was used to generate a 3D model of a site in the form of a point could. Images from the data set that was used to generate the point cloud are then interpolated with the scene, re-positioned and re-animated to reflexively show their original capture of the scene. There is an interest to find ways to justapose cinematic aesthetics with computational aesthetics, in the context of the gaze of future cinema technologies. Photogrammetry is a technique to create a computational scan of a scene using ordinary photographic cameras, a branch of technology known as reality computing. <img width="2559" height="1440" width_o="2559" height_o="1440" data-src="https://freight.cargo.site/t/original/i/c055ff486324e366969ee2704676576a76aea46f1816ce08aa9538dff2540eb1/cfr3.png" data-mid="40010976" border="0" data-scale="78" src="https://freight.cargo.site/w/1000/i/c055ff486324e366969ee2704676576a76aea46f1816ce08aa9538dff2540eb1/cfr3.png" /> Point cloud representation of an architectural structure generated through the process of photogrametry. <img width="2559" height="1439" width_o="2559" height_o="1439" data-src="https://freight.cargo.site/t/original/i/0027aaa101543594f31bfeee7233e9c320d1a7bcc8bfb156b03f0c170ba64e33/cfr4.png" data-mid="40010977" border="0" data-scale="78" src="https://freight.cargo.site/w/1000/i/0027aaa101543594f31bfeee7233e9c320d1a7bcc8bfb156b03f0c170ba64e33/cfr4.png" />Original camera positions are interpolated to the scene and reanimated. <img width="2555" height="1440" width_o="2555" height_o="1440" data-src="https://freight.cargo.site/t/original/i/85b506307c9531fe004bdc8dd8f149149fd590e46b5bd3db1d28c14d2b50deb0/cfr1.png" data-mid="40010974" border="0" data-scale="78" src="https://freight.cargo.site/w/1000/i/85b506307c9531fe004bdc8dd8f149149fd590e46b5bd3db1d28c14d2b50deb0/cfr1.png" />