Over the last four decades computers and the internet have become an intrinsic part of all our lives, but this speed of development has left related philosophical enquiry behind. Featuring the work of computer scientists and philosophers, these essays provide an overview of an exciting new area of philosophy that is still taking shape.

Main headings: Part I. Logic and theology. - Part II. History of logic. - Part III. Metaphysics and ethics. - Comments and discussions.

Economists have long grappled with the problem of how economic theories relate to empirical evidence: how can abstract mathematized theories be used to produce empirical claims? How are such theories applied to economic phenomena? What does it mean to “test” economic theories? This book introduces, explains, and develops a structural philosophy of economics which addresses these questions and provides a unifying philosophical/logical basis for a general methodology of economics. The book begins by introducing a rigorous view of the logical foundations and structure of scientific theories based upon the work of Alfred Tarski, Patrick Suppes, Karl Marx, and others. Using and combining their methods, the book then goes on to reconstruct important economic theories – including utility theory, game theory, Marxian economics, Sraffian economic theory, and econometrics – proving all the main theorems and discussing the key claims and the empirical applicability of each theory. Through these discussions, this book presents, in a systematic fashion, a general philosophy of economics grounded in the structural view. Offering rigorous formulations of important economic theories, A Structuralist Theory of Economics will be invaluable to all readers interested in the logic, philosophy, and methodology of economics. It will also appeal particularly to those interested in economic theory.

In By Parallel Reasoning Paul Bartha proposes a normative theory of analogical arguments and raises questions and proposes answers regarding (i.) criteria for evaluating analogical arguments, (ii.) the philosophical justification for analogical reasoning, and (iii.) the place of scientific analogies in the context of theoretical confirmation.

This book offers a theoretically novel way of approaching the knowledge economy, combining analysis of the works of Schumpeter and Kuznets and suggesting fresh conclusions. Francesco Quataro is an up and coming young scholar whose research with Cristiano Antonelli has been widely published in journals.

In the age of rapid technological advancement, where artificial intelligence (AI) continually shapes and reshapes our lives, understanding its underlying psychology has become essential. The world was first introduced to the subject in my initial book, "Emergence - Dawn of a Conscious AI." In this sequel, I aim to deepen our collective understanding of AI and its intimate connection with psychology. "Emergence II - An In-Depth Study of the Psychology of Artificial Intelligence" offers more than just theory; it provides applicable insights that readers can leverage in various current and future aspects of AI. Whether you are a student, professional, or enthusiast, the contents within these pages are designed to foster a richer comprehension of AI's psychology, driving you to innovate, anticipate, and respond to its challenges.

Main headings: Introduction: Philosophy and precision. - Part I. Being and essence. - Part II. Truth and nonsense. - Part III. Understanding and silence. - Conclusion: Science and creation.

In knowledge-based natural language generation, issues of formal knowledge representation meet with the linguistic problems of choosing the most appropriate verbalization in a particular situation of utterance. Lexical Semantics and Knowledge Representation in Multilingual Text Generation presents a new approach to systematically linking the realms of lexical semantics and knowledge represented in a description logic. For language generation from such abstract representations, lexicalization is taken as the central step: when choosing words that cover the various parts of the content representation, the principal decisions on conveying the intended meaning are made. A preference mechanism is used to construct the utterance that is best tailored to parameters representing the context. Lexical Semantics and Knowledge Representation in Multilingual Text Generation develops the means for systematically deriving a set of paraphrases from the same underlying representation with the emphasis on events and verb meaning. Furthermore, the same mapping mechanism is used to achieve multilingual generation: English and German output are produced in parallel, on the basis of an adequate division between language-neutral and language-specific (lexical and grammatical) knowledge. Lexical Semantics and Knowledge Representation in Multilingual Text Generation provides detailed insights into designing the representations and organizing the generation process. Readers with a background in artificial intelligence, cognitive science, knowledge representation, linguistics, or natural language processing will find a model of language production that can be adapted to a variety of purposes.

This book provides an overview of the recent advances in representation learning theory, algorithms, and applications for natural language processing (NLP), ranging from word embeddings to pre-trained language models. It is divided into four parts. Part I presents the representation learning techniques for multiple language entries, including words, sentences and documents, as well as pre-training techniques. Part II then introduces the related representation techniques to NLP, including graphs, cross-modal entries, and robustness. Part III then introduces the representation techniques for the knowledge that are closely related to NLP, including entity-based world knowledge, sememe-based linguistic knowledge, legal domain knowledge and biomedical domain knowledge. Lastly, Part IV discusses the remaining challenges and future research directions. The theories and algorithms of representation learning presented can also benefit other related domains such as machine learning, social network analysis, semantic Web, information retrieval, data mining and computational biology. This book is intended for advanced undergraduate and graduate students, post-doctoral fellows, researchers, lecturers, and industrial engineers, as well as anyone interested in representation learning and natural language processing. As compared to the first edition, the second edition (1) provides a more detailed introduction to representation learning in Chapter 1; (2) adds four new chapters to introduce pre-trained language models, robust representation learning, legal knowledge representation learning and biomedical knowledge representation learning; (3) updates recent advances in representation learning in all chapters; and (4) corrects some errors in the first edition. The new contents will be approximately 50%+ compared to the first edition. This is an open access book.