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.
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