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How generative AI systems capture a core function of language

Looking at the emergence of generative AI, Language Machines presents a new theory of meaning in language and computation, arguing that humanistic scholarship misconstrues how large language models (LLMs) function. Seeing LLMs as a convergence of computation and language, Leif Weatherby contends that AI does not simulate cognition, as widely believed, but rather creates culture. This evolution in language, he finds, is one that we are ill-prepared to evaluate, as what he terms “remainder humanism” counterproductively divides the human from the machine without drawing on established theories of representation that include both.

To determine the consequences of using AI for language generation, Weatherby reads linguistic theory in conjunction with the algorithmic architecture of LLMs. He finds that generative AI captures the ways in which language is at first complex, cultural, and poetic, and only later referential, functional, and cognitive. This process is the semiotic hinge on which an emergent AI culture depends. Weatherby calls for a “general poetics” of computational cultural forms under the formal conditions of the algorithmic reproducibility of language.

Locating the output of LLMs on a spectrum from poetry to ideology, Language Machines concludes that literary theory must be the backbone of a new rhetorical training for our linguistic-computational 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.
 

Futurists predict that in the next ten years the profession of “lawyer” will splinter into job titles like “legal process analyst” or “legal knowledge engineer.” And some in the field are already taking a proactive approach ⁠— in fact, more than two dozen law schools have developed innovation centers to explore artificial intelligence (AI) and the law. In a competitive marketplace, both firms and individuals need to familiarize themselves with the dazzling array of new products and enhanced features capable of improving efficiency. Written by leading practitioners and visionaries like Robert Ambrogi, this groundbreaking survey of current practices and future trends offers an incisive examination of the evolving roles for law librarians. Readers will learn how AI technology is changing law school curricula, lawyer practice, marketing, and other key aspects of the field through coverage of such topics as

• the benefits of AI to law librarianship, including areas like legal research, contract review, compliance, and administration, and their associated risks;
• four professional ethics rules that apply to the use or (non-use) of AI;
• how lawyers and staff work side by side with AI, utilizing intelligence like RAVN ACE or FastCase to attack the drudgery of due diligence and document review;
• surprising machine-learning insights from tokenizing, stemming, and lemmatizing the text of Shakespeare’s plays;
• the potential for chatbots and new natural language processing products to improve access to justice; and
• ways to develop sought-after skills through new technology departments, practice management groups, and legal innovation labs.

Reading this collection will give you a firm grasp of the innovations, tools, benefits, and risks of AI in law librarianship.

Exploring the influence of AI technologies on theories of reason, cognition, learning, and education

Learning Under Algorithmic Conditions presents twenty-seven concise essays that collectively chart the shifting terrain of learning in the age of artificial intelligence. Providing historical and philosophical context, this innovative volume features prominent scholars from the fields of media studies, philosophy, and education research, who shed light on how learning has become newly envisioned, machinic, and more-than-human. The contributors unravel various histories of machine intelligence and elucidate the current impact of machine learning technologies on practices of knowledge production. Teeming with theoretical and practical insights, Learning Under Algorithmic Conditions is an interdisciplinary guide for those working across the humanities and social sciences as well as anyone interested in understanding our changing social, political, and technical infrastructures.

Contributors: Craig Carson, Adelphi U; Felicity Coleman, U of the Arts London; Ed Dieterle; Shayan Doroudi, U of California, Irvine; David Gauthier, Utrecht U; Cathrine Hasse, Aarhus U; Talha Can İşsevenler, CUNY; Goda Klumbytė; Robb Lindgren, U of Illinois Urbana-Champaign; Michael Madiao; Henry Neim Osman; Luciana Parisi, Duke U; Carolyn Pedwell, Lancaster U; Arkady Plotnitsky, Purdue U; Julian Quiros, U of Pennsylvania; Sina Rismanchian; Warren Sack, U of California, Santa Cruz; R. Joshua Scannell, The New School; Gregory J. Seigworth, Millersville U; Rebecca Uliasz, U of Michigan; David Wagner, U of New Brunswick; Ben Williamson, U of Edinburgh.

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