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.
