{"resolved":true,"match_type":"uuid","source_type":"original_book","book_id":"06dd97ec-cce5-45df-9acf-40e36f07dab3","title":"[数字经济前沿]技术封建主义","author":null,"edition":null,"publisher":null,"year":null,"isbn":"9787300328508","requested_by":"printed_page","book":{"source_type":"original_book","book_id":"06dd97ec-cce5-45df-9acf-40e36f07dab3","id":"06dd97ec-cce5-45df-9acf-40e36f07dab3","uuid":"06dd97ec-cce5-45df-9acf-40e36f07dab3","title":"[数字经济前沿]技术封建主义","normalized_title":"数字经济前沿技术封建主义","author":null,"edition":null,"publisher":null,"year":null,"isbn":"9787300328508","isbns":["9787300328508"],"parsed":true,"ai_readable":true,"document_type":"book","page_count":259,"navigation_url":"https://workshop.closeclaw.top/ai/book/06dd97ec-cce5-45df-9acf-40e36f07dab3","title_url":"https://workshop.closeclaw.top/ai/title/%5B%E6%95%B0%E5%AD%97%E7%BB%8F%E6%B5%8E%E5%89%8D%E6%B2%BF%5D%E6%8A%80%E6%9C%AF%E5%B0%81%E5%BB%BA%E4%B8%BB%E4%B9%89","fixed_page_url_template":"https://workshop.closeclaw.top/ai/book/06dd97ec-cce5-45df-9acf-40e36f07dab3/page/{printed_page}","path_api_url_template":"https://workshop.closeclaw.top/api/ai/book/06dd97ec-cce5-45df-9acf-40e36f07dab3/page/{printed_page}","read_url":"https://workshop.closeclaw.top/api/ai/read/06dd97ec-cce5-45df-9acf-40e36f07dab3","page_url":"https://workshop.closeclaw.top/api/ai/books/06dd97ec-cce5-45df-9acf-40e36f07dab3/page","search_url":"https://workshop.closeclaw.top/api/ai/books/06dd97ec-cce5-45df-9acf-40e36f07dab3/search"},"page":{"printed_page":105,"pdf_page":145,"physical_index":145,"physical_index_base":0,"printed_page_status":"recognized","page_sha256":"63dee1269ebeafcea472c3222e613c6b47b9bd00dfdacd7a75eec441d61156c1"},"chapter":"第二章","content":"这种“强化学习”的方法是人工智能领域的一项成熟技术，应用领域十分广泛，包括机器人、计算机视觉和游戏，以及金融、教育、交通、能源网络和健康等。②机器对人类进行实验，目的是最大限度地获得非直接结果。与围棋比赛一样，计算机也在测试棋步和探索未知的策略。不同之处在于，这里的机器不是在与自己对弈。它测试对人类施加安排的有效性，目的是促使人类表现出研究赞助者希望促进的行为。例如，有一项实验旨在鼓励2型糖尿病患者参加体育锻炼。③机器被允许学习如何个性化地向参与者发送信息，尤其被允许调整信息的内容和信息发送的频率，无论该内容是积极的还是消极的，也无论是否包含社交元素。随着时间的推移，程序会根据每个人对所发信息的反应来调整与他们的互动。通过这种方式，机器学会了对信息进行个别调整，成功地显著提高了患者的活动水平。\n\n第二章 数字统治\n\n① David SILVER et al., «Mastering the game of Go with deep neural networks and tree search», Nature, vol. 529, n° 7587, 2016, p. 484-489.\n\n② Yuxi LI, «Deep reinforcement learning», arXiv. org, 15 octobre 2018, en ligne.\n\n③ Irit HOCHBERG et al., «Encouraging physical activity in patients with diabetes through automatic personalized feedback via reinforcement learning improves glycemic control: table 1», Diabetes Care, vol. 39, n° 4, 2016, p. 59-60.\n\n105","previous_page":104,"next_page":106}