{"resolved":true,"match_type":"uuid","source_type":"original_book","book_id":"a2eab101-adfc-4a0d-8142-63a4d11cee02","title":"人工智能和资本主义的未来","author":null,"edition":null,"publisher":null,"year":null,"isbn":"9780745338613","requested_by":"printed_page","book":{"source_type":"original_book","book_id":"a2eab101-adfc-4a0d-8142-63a4d11cee02","id":"a2eab101-adfc-4a0d-8142-63a4d11cee02","uuid":"a2eab101-adfc-4a0d-8142-63a4d11cee02","title":"人工智能和资本主义的未来","normalized_title":"人工智能和资本主义的未来","author":null,"edition":null,"publisher":null,"year":null,"isbn":"9780745338613","isbns":["9780745338613","9780745338606","9781786803955","9781786803979","9781786803962"],"parsed":true,"ai_readable":true,"document_type":"book","page_count":225,"navigation_url":"https://workshop.closeclaw.top/ai/book/a2eab101-adfc-4a0d-8142-63a4d11cee02","title_url":"https://workshop.closeclaw.top/ai/title/%E4%BA%BA%E5%B7%A5%E6%99%BA%E8%83%BD%E5%92%8C%E8%B5%84%E6%9C%AC%E4%B8%BB%E4%B9%89%E7%9A%84%E6%9C%AA%E6%9D%A5","fixed_page_url_template":"https://workshop.closeclaw.top/ai/book/a2eab101-adfc-4a0d-8142-63a4d11cee02/page/{printed_page}","path_api_url_template":"https://workshop.closeclaw.top/api/ai/book/a2eab101-adfc-4a0d-8142-63a4d11cee02/page/{printed_page}","read_url":"https://workshop.closeclaw.top/api/ai/read/a2eab101-adfc-4a0d-8142-63a4d11cee02","page_url":"https://workshop.closeclaw.top/api/ai/books/a2eab101-adfc-4a0d-8142-63a4d11cee02/page","search_url":"https://workshop.closeclaw.top/api/ai/books/a2eab101-adfc-4a0d-8142-63a4d11cee02/search"},"page":{"printed_page":78,"pdf_page":86,"physical_index":86,"physical_index_base":0,"printed_page_status":"recognized","page_sha256":"d915403da4ea92d330bb640da64a485d8de99f800394752f891f6c7a0af8820c"},"chapter":"Digital Barricades: Interventions in Digital Culture and Politics","content":"Such activities illustrate ‘the paradox of automation’s last mile: as AI makes progress, it also results in the rapid creation and destruction of temporary labor markets for new types of humans-in-the-loop tasks’ (Gray and Suri 2017). There is a further twist in this paradox: data cleaning and monitoring have themselves become targets for ML automation. Amazon’s Mechanical Turk Human micro-tasking is referred to by the corporation as ‘artificial artificial intelligence’ (Reese and Heath 2016); the irony is that ‘turkers’ train real artificial intelligences that may one day replace them (Hook 2016). Facebook and Google executives are explicit that their goal is to eventually automate content moderation (Alba 2017; Nieva 2018). Data scientists have declared data cleaning ‘a machine learning problem that needs machine learning help’ (Ilyas 2018). Clickworkers are contributing data sets of their own digitally recorded labour as training material for future AI.\n\nML thus exemplifies what Tim Jordan (2015: 32–8) terms ‘recursion’, the process by which information capital feeds into itself. Fear of automation has worked backed to the elite strata of software developers. AI industry workers exhibit a paradoxical relationship to automation in their work; while they are wary of it, they also describe it as necessary and even desirable (Steinhoff 2019b). This then spurs further disagreement as to whether such technologies will enhance or extinguish the work of software engineers (Shanin 2018; Vorhies 2016a, 2017). In 2016, 550 developers were asked to identify the ‘most worrisome thing in their careers’ from a list of possible concerns: ‘I and my development efforts are replaced by artificial intelligence’ was the most popular answer (Thibodeau 2016). Some developers dismiss such alarms, believing AI industry growth will only increase demand for human coding and design. Others foresee software developers increasingly assisted by AI ‘bots’ that automate code checking, distribution and repair, and will increasingly ‘spin up’ applications in response to natural language commands without developers ‘even touching the keyboard’, escalating to a point where the ‘entire software development lifecycle will be only a high-level description’ (Despoudis 2017). Such discussions have been further stirred by the recent BAYOU ML applications, developed at Rice University for the US Department of Defense; this is a neural network that ‘trained itself’ on millions of lines of human-written code on GitHub, and can reportedly recommend chunks of code to developers on the basis of very brief keyword instructions, thereby taking initial steps towards ML-designed ML systems (Rayome 2018).\n\n78\n\ninhuman power","previous_page":77,"next_page":79}