{"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":164,"pdf_page":172,"physical_index":172,"physical_index_base":0,"printed_page_status":"recognized","page_sha256":"53b25d5cd7586e4c6c281f9829f0ffc1031a7d97d3d90cd7fb0f9c078b277456"},"chapter":"Digital Barricades: Interventions in Digital Culture and Politics","content":"3. For a recent review of various attempts to complete Marx’s thought on the state, see O’Kane (2014), and for discussion of the state’s role in creating and maintaining the general conditions of production, Läpple (1973).\n\n4. However, the very fact that the privatization of initially state-led technologies is so advanced in the US creates some problems for the US government. The big-tech five, Google, Amazon, Facebook, Apple and Microsoft together spend almost as much on non-defence-related scientific research as the US federal government (Manjoo 2017). In instances where there is conflict between the US government and tech companies, as, for example, between the FBI and Apple over encryption, it is by no means certain that the state holds superior know-how.\n\n5. When Marx discussed the particular conditions of production, he referred only to those elements of production that assumed the form of constant capital, and he gave special attention to fixed capital in particular. While this book focuses primarily on the form of fixed capital, it is important to remember that the economic form of fixed capital cannot be reduced to machinery. Buildings, tools, internal transportation networks, and facilities for storing the productive reserve and finished commodities all function as fixed capital in the same way as machinery does (Marx 1990: 510; 1992: 201).\n\n6. Even the ostensibly human internet is increasingly travelled by machines. According to Zeifman (2017), humans only accounted for 48.2 per cent of traffic on the internet in 2016 with the rest being generated by various bots.\n\n7. Post-operaismo thinkers have generally not directly addressed the AI issue, with the exception of Matteo Pasquinelli who has elaborated the necessity of understanding information machines and data, especially metadata, as key components of labour and capital in twenty-first-century capitalism and recognizes that capital and AI have deep and evolving affinities. His work represents an attempt to come to terms with some of the contradictions AI presents to post-operaismo thought, though he has yet to present a systematic critique. We therefore look forward eagerly to his upcoming monograph on capital and computation (2019).\n\nCHAPTER 2\n\n1. For a sample, see the papers from the conference ‘The Economics of Artificial Intelligence: An Agenda’, 13–14 September 2017, available from the National Bureau of Economic Research at https://www.nber.org/books/ agra-1, published in Agrawal, Gans and Goldfarb (2019).\n\n2. Leo Impett (2018), however, argues that removing algorithmic bias from Deep Learning (DL) AI is all but impossible, as such systems automatically vectorize any and all data; in GOFAI and normal ML, it is possible to remove biases like gender, race, credit score, etc.; DL algorithms take everything into account – which is also why they are so powerful.\n\n164\n\ninhuman power","previous_page":null,"next_page":165}