{"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":44,"pdf_page":52,"physical_index":52,"physical_index_base":0,"printed_page_status":"recognized","page_sha256":"8ab080f057a1cb772602fe19d5b7d75a6dc1dd75535c559faaa72070b72cce56"},"chapter":"Digital Barricades: Interventions in Digital Culture and Politics","content":"AI BUBBLE?\n\nThe latter half of the 2010s saw increasingly widespread speculation on the emergence of an ‘AI bubble’ (Press 2018). In a 2017 report, the business consultancy Gartner gave warning: ‘As AI accelerates up the Hype Cycle, ... most vendors are focused on the goal of simply building and marketing an AI-based product rather than first identifying needs, potential uses and the business value to customers’ (Hernandez 2017). It cautioned its business readers not to fall for ‘AI washing’ or exaggerated claims about the capabilities of AI systems. More luridly, a 2018 business report predicted that while AI would eventually ‘mature’ as a commercial sector, ‘the field will be littered with corpses on the way’ (Riot Research 2018). Might the AI industry descend into another ‘AI winter’ before the technology is sufficiently generalized so as to constitute a part of the general conditions of production?\n\nThe bursting of an ‘AI bubble’ and the failure of many of the recent entrants into the AI industry would not necessarily spell the end of AI-capital. The collapse of overvalued start-ups, and even large, established firms, making room for fresh entrants learning from their predecessors’ failures, has for centuries been a normal cyclical feature of capital’s innovations, from the railway and telegraph onward. This is all part and parcel of capital accumulation. The most germane recent example is the bursting of the US ‘dot.com’ bubble in 2000, which laid waste to many early e-commerce attempts and, by knock-on effect, threw the telecommunications industry into crisis. For a few years this disaster stalled investment in new digital business, until the emergence of Google, Facebook and other Web 2.0 companies, who, with revised business models, joined the big survivors of the bust, such as Amazon and Microsoft, in a new and even larger wave of digital commodification. A similar crisis could be a mere bump in the road along which AI advances.\n\nThere are, however, other possibilities that might more deeply disturb booming forecasts for the AI industry. One would be the discovery of serious, endemic problems in the technology itself. Roman V. Yampolskiy (n.d.), a researcher associated with the AI Safety movement, has compiled a short inventory of failures in AI. Perhaps the most serious of these dates from 2013: ‘Object recognition neural networks saw phantom objects in particular noise images.’ This refers to a well-documented tendency of ML object recognition systems to confuse, but report with very high confidence, certain abstract patterns with real-life entities such as peacocks or leopards. This bizarre quirk appears to derive from such systems’ interpretation of radically new objects as merely extreme examples of the data sets on which they have been trained. The most immediate concern arising from this is that image-recognition systems used for military and security purposes are susceptible to malign spoofing by ‘adversarial images’ (Nguyen, Yosinski and Clune 2015). Even more troubling is the implication that neural networks are opaque to their producers, and can suddenly throw up entirely unanticipated results (Scharre 2018). Such disquiet has yet to seriously dampen AI enthusiasm. But disastrous AI errors might dent business confidence. A series of fatal autonomous vehicle accidents in 2018 cast a pall, however temporary, over self-driving car research by reminding corporations and publics of the complex and costly liabilities such technology involves.\n\n44\n\ninhuman power","previous_page":43,"next_page":45}