Yair Neuman – författare

Reviving the Living: Meaning Making in Living Systems presents a novel perspective that relates to current biological knowledge and issues. Written by polymath Dr. Yair Neuman, the book challenges the dogmas that frame our understanding of living systems and presents a radical alternative approach to understanding the world around us, one that avoids the pitfalls of non-scientific perspectives such as Vitalism and Creationism.

In this thought provoking and iconoclastic manuscript, Neuman follows the footsteps of Gregory Bateson, Mikhail Bakhtin, Michael Polanyi and others, to suggest that living systems are meaning making systems. The book delves into the unique processes of meaning making that characterize organisms as a unique category of nature, and offers new and fascinating insights into a variety of enigmatic biological phenomena from immune memory to hidden life (cryptobiosis). It consists of four parts divided into 18 chapters and covers topics ranging from reductionism and its pitfalls to genetics; why organisms are irreducible; immunology; meaning making in language and biology; meaning-bridging the gap between physics and semantics; context and memory; and the poetry of living. Core concepts and themes are illustrated using examples based in current science.

This text would be of high interest to biologists, philosophers, cognitive scientists, psychologists, and semioticians, as well as to any reflective individual who is willing to examine the realm of the living from a novel and fascinating perspective.

Searching for a needle in a haystack is an important task in several contexts of data analysis and decision-making. Examples include identifying the insider threat within an organization, the prediction of failure in industrial production, or pinpointing the unique signature of a solo perpetrator, such as a school shooter or a lone wolf terrorist. It is a challenge different from that of identifying a rare event (e.g., a tsunami) or detecting anomalies because the "needle" is not easily distinguished from the haystack. This challenging context is imbued with particular difficulties, from the lack of sufficient data to train a machine learning model through the identification of the relevant features and up to the painful price of false alarms, which might cause us to question the relevance of machine learning solutions even if they perform well according to common performance criteria. In this book, Prof. Neuman approaches the problem of finding the needle by specifically focusing on the human factor, from solo perpetrators to insider threats. Providing for the first time a deep, critical, multidimensional, and methodological analysis of the challenge, the book offers data scientists and decision makers a deep scientific foundational approach combined with a pragmatic practical approach that may guide them in searching for a needle in a haystack.

Searching for a needle in a haystack is an important task in several contexts of data analysis and decision-making. Examples include identifying the insider threat within an organization, the prediction of failure in industrial production, or pinpointing the unique signature of a solo perpetrator, such as a school shooter or a lone wolf terrorist. It is a challenge different from that of identifying a rare event (e.g., a tsunami) or detecting anomalies because the "needle" is not easily distinguished from the haystack. This challenging context is imbued with particular difficulties, from the lack of sufficient data to train a machine learning model through the identification of the relevant features and up to the painful price of false alarms, which might cause us to question the relevance of machine learning solutions even if they perform well according to common performance criteria. In this book, Prof. Neuman approaches the problem of finding the needle by specifically focusing on the human factor, from solo perpetrators to insider threats. Providing for the first time a deep, critical, multidimensional, and methodological analysis of the challenge, the book offers data scientists and decision makers a deep scientific foundational approach combined with a pragmatic practical approach that may guide them in searching for a needle in a haystack.

This concise volume offers an accessible introduction to state-of-the-art artificial intelligence (AI) language models, providing a platform for their use in textual interpretation across the humanities and social sciences.

The book outlines the affordances of new technologies for textual analysis, which has historically employed established approaches within the humanities. Neuman, Danesi, and Vilenchik argue that these different forms of analysis are indeed complementary, demonstrating the ways in which AI-based perspectives echo similar theoretical and methodological currents in traditional approaches while also offering new directions for research. The volume showcases examples from a wide range of texts, including novels, television shows, and films to illustrate the ways in which the latest AI technologies can be used for "dialoguing" with textual characters and examining textual meaning coherence.

Illuminating the potential of AI language models to both enhance and extend research on the interpretation of texts, this book will appeal to scholars interested in cognitive approaches to the humanities in such fields as literary studies, discourse analysis, media studies, film studies, psychology, and artificial intelligence.

This concise volume offers an accessible introduction to state-of-the-art artificial intelligence (AI) language models, providing a platform for their use in textual interpretation across the humanities and social sciences.

The book outlines the affordances of new technologies for textual analysis, which has historically employed established approaches within the humanities. Neuman, Danesi, and Vilenchik argue that these different forms of analysis are indeed complementary, demonstrating the ways in which AI-based perspectives echo similar theoretical and methodological currents in traditional approaches while also offering new directions for research. The volume showcases examples from a wide range of texts, including novels, television shows, and films to illustrate the ways in which the latest AI technologies can be used for "dialoguing" with textual characters and examining textual meaning coherence.

Illuminating the potential of AI language models to both enhance and extend research on the interpretation of texts, this book will appeal to scholars interested in cognitive approaches to the humanities in such fields as literary studies, discourse analysis, media studies, film studies, psychology, and artificial intelligence.

Artificial intelligence (AI) and specifically large language models (LLMs) revolutionize our lives with new technologies appearing at an unprecedented rate. These technologies can potentially change how we understand human conversations, from the dialogues of married couples to diplomatic conversations.

This book explains how to use LLMs to analyze human conversations and how better LLMs can be developed by incorporating a deep theoretical understanding.

Drawing on case studies from Pulp Fiction to diplomatic meetings, the book provides detailed, approachable, and theoretically grounded explanations of how LLMs can help us understand conversations.

Artificial intelligence (AI) and specifically large language models (LLMs) revolutionize our lives with new technologies appearing at an unprecedented rate. These technologies can potentially change how we understand human conversations, from the dialogues of married couples to diplomatic conversations.

This book explains how to use LLMs to analyze human conversations and how better LLMs can be developed by incorporating a deep theoretical understanding.

Drawing on case studies from Pulp Fiction to diplomatic meetings, the book provides detailed, approachable, and theoretically grounded explanations of how LLMs can help us understand conversations.