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A revealing and surprising origin story, showing how attempts to render human speech and language computable led from the era of big data to today’s AI.
 
Since the advent of computers, society has fantasized about conversing with machines. In this eye-opening book, technology expert Xiaochang Li shows readers how that dream both fueled the demand for data and set the stage for today’s generative AI. With original research and clear explanations, Li elucidates the origins of what’s known as natural language processing (NLP) and the heated twentieth-century debates between computer scientists, linguists, and communication engineers that shaped today’s technology. Starting with early devices that recorded, analyzed, and attempted to interpret human speech, she demonstrates how computer speech recognition, particularly efforts led by Bell Labs and IBM, advanced technology by deemphasizing linguistic meaning in favor of statistical prediction. In other words, researchers gradually abandoned systems that sought to understand human language, opting instead for work-arounds that simply predicted patterns in speech and text data. That solution became incredibly and surprisingly adaptable. As Li reveals, transforming linguistic questions into engineering ones ushered in the routine operation of search engines, spam filters, and the varied content sorting and recommendation mechanisms that regulate the access, circulation, and legitimacy of information across every platform. But this has all come at the cost of forever requiring copious and ever-growing amounts of new data.
 
At its core, Divination Engines illuminates how the artifacts of human communication—speech, text, and images—have become both the fodder for and products of computers. This connection between communication and computation, Li shows, has given rise to data-driven analytics, machine learning, and today’s algorithmic culture.

A wide-ranging history of the algorithm.

Bringing together the histories of mathematics, computer science, and linguistic thought, Language and the Rise of the Algorithm reveals how recent developments in artificial intelligence are reopening an issue that troubled mathematicians well before the computer age: How do you draw the line between computational rules and the complexities of making systems comprehensible to people? By attending to this question, we come to see that the modern idea of the algorithm is implicated in a long history of attempts to maintain a disciplinary boundary separating technical knowledge from the languages people speak day to day.
 
Here, Jeffrey M. Binder offers a compelling tour of four visions of universal computation that addressed this issue in very different ways: G. W. Leibniz’s calculus ratiocinator; a universal algebra scheme Nicolas de Condorcet designed during the French Revolution; George Boole’s nineteenth-century logic system; and the early programming language ALGOL, short for algorithmic language. These episodes show that symbolic computation has repeatedly become entangled in debates about the nature of communication. Machine learning, in its increasing dependence on words, erodes the line between technical and everyday language, revealing the urgent stakes underlying this boundary.
 
The idea of the algorithm is a levee holding back the social complexity of language, and it is about to break. This book is about the flood that inspired its construction.

What kind of meaning can machines make—and why does it matter that it’s not the same as ours?

Anthropologist Paul Kockelman's Last Words offers a rigorous but accessible account of how large language models actually work—and why the meaning they produce is fundamentally different from human meaning-making. Drawing on the semiotics of C.S. Peirce, Kockelman’s witty and insightful pamphlet shows how LLMs are trained to predict word-word relations, not word-world relations, which explains both their uncanny fluency and their systematic blind spots. The result is a compact, essential guide to cutting through the hype: not a dismissal of AI, but a precise account of what it can and cannot do—and who profits from the confusion.