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商品描述
If machine learning transforms the nature of knowledge, does it also transform the practice of critical thought?
Machine learning -- programming computers to learn from data -- has spread across scientific disciplines, media, entertainment, and government. Medical research, autonomous vehicles, credit transaction processing, computer gaming, recommendation systems, finance, surveillance, and robotics use machine learning. Machine learning devices (sometimes understood as scientific models, sometimes as operational algorithms) anchor the field of data science. They have also become mundane mechanisms deeply embedded in a variety of systems and gadgets. In contexts from the everyday to the esoteric, machine learning is said to transform the nature of knowledge. In this book, Adrian Mackenzie investigates whether machine learning also transforms the practice of critical thinking.
Mackenzie focuses on machine learners -- either humans and machines or human-machine relations -- situated among settings, data, and devices. The settings range from fMRI to Facebook; the data anything from cat images to DNA sequences; the devices include neural networks, support vector machines, and decision trees. He examines specific learning algorithms -- writing code and writing about code -- and develops an archaeology of operations that, following Foucault, views machine learning as a form of knowledge production and a strategy of power. Exploring layers of abstraction, data infrastructures, coding practices, diagrams, mathematical formalisms, and the social organization of machine learning, Mackenzie traces the mostly invisible architecture of one of the central zones of contemporary technological cultures.
Mackenzie's account of machine learning locates places in which a sense of agency can take root. His archaeology of the operational formation of machine learning does not unearth the footprint of a strategic monolith but reveals the local tributaries of force that feed into the generalization and plurality of the field.
商品描述(中文翻譯)
如果機器學習改變了知識的本質，那麼它是否也改變了批判性思考的實踐方式呢？
機器學習——通過將計算機編程以從數據中學習——已經在科學領域、媒體、娛樂和政府中得到廣泛應用。醫學研究、自動駕駛車輛、信用交易處理、電腦遊戲、推薦系統、金融、監控和機器人技術都使用了機器學習。機器學習設備（有時被理解為科學模型，有時被理解為操作算法）是數據科學領域的基石。它們也成為了深深嵌入各種系統和設備中的平凡機制。從日常生活到專業領域，機器學習被認為改變了知識的本質。在這本書中，Adrian Mackenzie探討了機器學習是否也改變了批判性思考的實踐方式。
Mackenzie專注於機器學習者——無論是人類和機器還是人機關係——位於設置、數據和設備之間。這些設置範圍從fMRI到Facebook；數據可以是從貓圖像到DNA序列的任何東西；設備包括神經網絡、支持向量機和決策樹。他審查了具體的學習算法——編寫代碼和關於代碼的文章——並發展了一種操作的考古學，該考古學將機器學習視為一種知識生產和權力策略，遵循福柯的觀點。通過探索抽象層次、數據基礎設施、編碼實踐、圖表、數學形式化和機器學習的社會組織，Mackenzie追蹤了當代技術文化中一個中心區域的大部分隱形架構。
Mackenzie對機器學習的描述定位了一個可以培養代理感的地方。他對機器學習操作形成的考古學並未揭示戰略巨石的足跡，而是揭示了滋養該領域普遍化和多樣性的地方支流。
