{"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":14,"pdf_page":22,"physical_index":22,"physical_index_base":0,"printed_page_status":"recognized","page_sha256":"30975d209d4080598d6ab0f5f5fc9c13fd5898791b4c12710edf316be1b1ceda"},"chapter":"Digital Barricades: Interventions in Digital Culture and Politics","content":"It is useful to distinguish between the three broad types of machine learning – supervised, unsupervised and reinforcement learning. Supervised learning has been the most successful so far. In this approach, input data is labelled by human teachers, usually in terms of categories, and the system learns those categories by discerning patterns across the supplied examples. Given enough photos of red hexagonal signs with the word STOP on them, in various visibility conditions, and from various angles, a supervised learning system can learn the concept of a stop sign.\n\nHowever, because of the necessity of labelling, supervised learning entails a lot of human labour. This has driven companies to develop techniques of unsupervised learning to enable a network to generate categories and labels on its own (Alpaydin 2016: 117). The idea is that with exposure to enough data, the system will identify ‘incredibly sophisticated and complex correlations’ across the data set (Kaplan 2016: 30). In so doing, it may be said to generate its own concept of a stop sign. Some deep learning pioneers argue this will eventually become the central ML approach because it mimics how humans and animals evolved to learn; not by being told what everything in the world is called, but through observing it (LeCun, Bengio and Hinton 2015: 442).\n\nReinforcement learning lies somewhere in between supervised and unsupervised learning. The pioneers of this approach describe it as ‘learning what to do … so as to maximize a numerical reward signal. The learner is not told which actions to take … but instead must discover which actions yield the most reward by trying them’ (Sutton and Barto 1998: 127). Reinforcement learning was thought to be limited to simple domains, until 2013 when the UK firm DeepMind combined it with unsupervised learning to teach a system to play Atari games with superhuman skill, without programming any knowledge about the games into the system, and giving it access only to the score of the games and the pixel information displayed on the screen (Knight 2017). The same combination enabled AlphaGo’s win over Go master and world champion Lee Sedol.\n\nAs Kaplan emphasizes, the learning in ML should be understood as ‘extract[ing] patterns from data’ (2016: 27).<sup>4</sup> Instead of being built top-down as a set of rules for handling data, ML systems go bottom-up: ‘learning algorithms … are algorithms that make other algorithms … computers [that] write their own programs, so we don’t have to … [it is] the inverse of programming’ (Domingos 2015: 6–7). This is why ML advocates see it as a Copernican revolution in programming. If a system can learn, the designer does not need to anticipate and program a solution for all potential situations the system is exposed to (Alpaydin 2016: 17). Ideally, the system will develop its own solutions and ‘the data itself … defines what to do next’ (Alpaydin 2016: 11).\n\n14\n\ninhuman power","previous_page":13,"next_page":15}