{"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":34,"pdf_page":42,"physical_index":42,"physical_index_base":0,"printed_page_status":"recognized","page_sha256":"8a96fb580d5574481b2ce11bbc1f460979825db9a2a08ad641050e74c3e7580c"},"chapter":"Digital Barricades: Interventions in Digital Culture and Politics","content":"HANA, a ML-powered cloud database platform that enables behemoths like Walmart to monitor their entire organization’s functioning in fine-grained, real-time detail (Ruth 2017), and Andrew Ng’s start-up Landing.ai (founded in 2017) which aims to totally overhaul industrial manufacturing by providing ‘AI-powered adaptive manufacturing, automated quality control, predictive maintenance, and more’ (Landing n.d.). In Department 2, examples include various consumer commodities like Amazon Home and similar devices. Marketed as a ‘smart speaker’, Home is a user voice interface to the Alexa digital personal assistant that enables a variety of home automation and organizational tasks. AI is also found in other smart devices like phones and TVs and is also ‘given away’ as a component of free product-services such as Facebook, Twitter or YouTube where ML-based recommender systems curate timelines and give users suggestions on what to watch or listen to next. In turn, these systems gather customer data to fuel advertising revenues. However, as we will see, production of both Department 1 and Department 2 AI is often dominated by the same oligopolistic corporations, and may also be interconnected in a variety of ways, including the use of shared cloud computing facilities.\n\nFrom 2015 on there has been a rapid escalation of corporate investment in AI research, venture funding of ML start-ups, and competitive hiring of AI talent as well as lots of acquisition and merger activity. Measuring the scale of this activity is difficult. According to one analysis, the AI industry had a revenue of \\$126 billion in 2015 and is projected to grow to \\$3,061 billion by 2024 (Statista 2016: 9), but another reckons worldwide spending on AI stood at only \\$19.1 billion in 2018, an increase of 54.2 per cent over 2017, and predicts it will reach a mere \\$52.2 billion by 2021 (International Data Corporation 2018). The Economist (2017a) calculates that in 2017 companies globally spent around \\$21.3 billion in mergers and acquisitions related to AI – 26 times more than in 2015. While such conflicting estimates (often manifestly driven by the self-interest of AI vendors and business consultancies) are confusing, it is clear that AI has seized the imagination of advanced capital’s representatives (see also Press 2018). As The Economist (2017a) puts it, ‘Fueled by rivalry, high hopes and hype, the AI boom can feel like the first California gold rush.’\n\nCorporate competition for ML experts is ferocious. One study, based on LinkedIn profile data, puts the number of PhD educated people ‘capable of working in AI research and applications’ at 22,000, with only 3,074 currently looking for work (Gagné 2018). Demand far exceeds supply (Economist 2017b). US information capitalists are in competition both with new contenders – such as major auto companies with autonomous vehicle projects – but also now with China’s tech companies, some of which have set up subsidiaries in Silicon Valley. As hiring top talent is seen as crucial for the success of AI-capital, this competition has ‘set off a trend of firms plundering academic departments to hire professors and graduate students before they finish their degrees’ and created an atmosphere in which job fairs resemble frantic ‘Thanksgiving Black Friday sales at Walmart’ (Economist 2017b). This competition for ML talent also means that wages are high.\n\n34\n\ninhuman power","previous_page":33,"next_page":35}