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？**
機器學習——讓電腦從數據中學習的程式設計——已經遍及科學領域、媒體、娛樂和政府。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在使用機器學習。機器學習設備（有時被理解為科學模型，有時作為操作算法）是數據科學領域的基石。它們也已成為深深嵌入各種系統和設備中的平凡機制。在從日常到深奧的背景中，機器學習被認為改變了知識的本質。在這本書中，Adrian Mackenzie 探討機器學習是否也改變了批判性思考的實踐。
Mackenzie 專注於機器學習者——無論是人類與機器，還是人機關係——這些學習者位於各種環境、數據和設備之中。這些環境從功能性磁共振成像（fMRI）到 Facebook；數據從貓的圖片到 DNA 序列；設備包括神經網絡、支持向量機和決策樹。他考察特定的學習算法——編寫代碼和撰寫代碼的過程——並發展出一種操作考古學，根據福柯的觀點，將機器學習視為一種知識生產形式和權力策略。Mackenzie 探索抽象層次、數據基礎設施、編碼實踐、圖表、數學形式化以及機器學習的社會組織，追溯當代技術文化中心區域的主要隱形架構。
Mackenzie 對機器學習的描述定位了可以扎根的代理感。他對機器學習操作形成的考古學並未挖掘出一個戰略性獨石的足跡，而是揭示了流入該領域的普遍化和多樣性的當地力量支流。
