{"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":42,"pdf_page":50,"physical_index":50,"physical_index_base":0,"printed_page_status":"recognized","page_sha256":"cf099babb929733d3ab5ec68a125982497242c41c9c27144bedf4b411dde0dd7"},"chapter":"Digital Barricades: Interventions in Digital Culture and Politics","content":"EDGE, CLOUD AND ECONOMIES OF SCALE\n\nEssential to the AI industry, whether at the level of firm or nation, is the expensive hardware on which AI runs. Today this predominantly takes the form of ‘the cloud’ – vast, energy guzzling data centres that users can pay to access over the internet (Mosco 2014). As the availability of bandwidth and processing power have increased, the cloud has become available not only for storage, database and computing functions, but also in the past few years for cloud AI or ‘AI as a service’ (MSV 2018). AI-infused consumer devices and services send data to the cloud where the actual AI processing is done. The cloud also enables the insertion of ML techniques (like image and voice recognition) into websites or programs, as well as the online building of ML models. The intense computational requirements for training deep ML models mean that few small companies can afford to purchase the requisite hardware and instead buy computing time from cloud providers. Tractica (2018) reports that the AI industry has seen a 300,000 times increase in computing power requirements since 2012, making cloud AI a space of aggressive competition. Amazon Web Services and Microsoft Azure have been the top performers, with Google Cloud, IBM and Baidu Cloud attempting to gain market share. Cloud AI thus promises to make ML available to companies well beyond the inner circles of the AI industry, but already ownership of the cloud is consolidated in the hands of a ‘very select few’, with the tech giants being the dominant providers (Miller 2018).\n\nThe cloud is complemented by an emerging technique called edge computing. Edge computing is an approach in which some processing is done locally on devices rather than sent to the cloud. Tractica (2018) estimates that the amount of AI edge devices shipped will increase from ‘161.4 million units in 2018 to 2.6 billion units worldwide annually by 2025’. Edge computing offers advantages over the cloud when it comes to bandwidth, network latency issues and security. In applications such as autonomous vehicles, a bit of lag could be disastrous. In addition, keeping data on board a device offers obvious security benefits against hackers. As computational requirements and numbers of users grow, edge computing also becomes attractive to cloud AI providers in so far as it can reduce the load on their clouds (Miller 2018). No one, however, expects the edge to replace the cloud. The two work in tandem. Rather than a decentralized disruptor of the cloud, the edge is likely to become another axis of competition for the tech giants, who are already invested heavily in it.\n\n42\n\ninhuman power","previous_page":41,"next_page":43}