Jordi Vallverdú – författare

This edited volume is about how unprejudiced approaches to real human cognition can improve the design of AI. It covers many aspects of human cognition and across 12 chapters the reader can explore multiple approaches about the complexities of human cognitive skills and reasoning, always guided by experts from different but complimentary academic fields.A central concept is explained: blended cognition, the natural skill of human beings for combining constantly different heuristics during their several task-solving activities. Something that was sometimes observed like a problem as “bad reasoning”, is now the central key for the understanding of the richness, adaptability and creativity of human cognition. The topic of this book connects in a significant way with the disciplines of psychology, neurology, anthropology, philosophy, logics, engineering, logics, and AI. In a nutshell: understanding better humans for designing better machines.Any person with interests on natural and artificial reasoning should read this book as a primary source of inspiration and a way to achieve a critical thinking on these topics.

This interdisciplinary volume gathers selected, refereed contributions on various aspects of public health from several disciplines and research fields, including the philosophy of science, epidemiology, statistics and ethics.

This interdisciplinary volume gathers selected, refereed contributions on various aspects of public health from several disciplines and research fields, including the philosophy of science, epidemiology, statistics and ethics.

Why AI does not include gender in its agenda? Highly innovative and the first step in a series of future studies in this field, this book covers several voices, topics, and perspectives that allow the reader to understand the necessity to include into the AI research agenda such points of view and also to attract more women to this field.

Why AI does not include gender in its agenda? Highly innovative and the first step in a series of future studies in this field, this book covers several voices, topics, and perspectives that allow the reader to understand the necessity to include into the AI research agenda such points of view and also to attract more women to this field.

This book seeks to explore the uncharted territory of the intersection between death and technology, particularly within the realms of robotics, artificial intelligence (AI), and generative AI. Through interdisciplinary collaboration and rigorous philosophical inquiry, it discusses a wide range of challenges and realities stemming from the new context of death, i.e. in the age of technology, providing answers and perspectives regarding autonomy, morality, and the nature of existence.

instead, it offers a set of practical strategies to work with them—mobilizing the capabilities of generative AI to support causal reasoning tailored to disciplinary norms and constraints. Generative AI can engage in multimodal causal inference—connecting language with images, charts, simulations, and numerical data.

This book analyzes the origins of statistical thinking as well as its related philosophical questions, such as causality, determinism or chance. Despite the mathematical nature of the topic, no statistical background is required, making the book a valuable read for anyone interested in the history of statistics and human cognition.

How can we teach machine learning to identify causal patterns in data?  This book explores the very notion of “causality”, identifying from a naturalistic and evolutionary perspective how living systems deal with causal relationships. At the same time, using this knowledge to identify the best ways to apply such biological models in machine learning scenarios.One of the more fundamental challenges for AI experts is to design machines that can understand the world, identifying the basic rules that govern reality.  Statistics are powerful and fundamental for this process, but they are only one of the necessary tools. Counterfactual thinking is the other part of the necessary process that will help machines to become intelligent. This book explains the paths that can lead to algorithmic causality.It is essential reading for those who are not afraid of thinking at the interface of various academic disciplines or fields (AI, machine learning, philosophy,  neuroscience, anthropology, psychology, computer sciences), and who are interested in the analysis of causal thinking and the ways in which cognitive systems (natural or artificial) can act in order to understand their environment.Professor Vallverdú is currently working on biomimetic cognitive architectures and multicognitive systems. His research has explored two main areas: epistemology and cognition. Since his early Ph.D. research on epistemic controversies, he has analyzed several aspects of computational epistemology.His latest research has focused on the causal challenges of machine learning techniques, particularly deep learning. One of his most promising advances is statistics meets causal graph reasoning (via Directed Acyclic Graphs), which still has several conceptual paths that need to be explored and identified. Counterfactual reasoning is a fundamental part of these open debates, which are under the analysis of Prof. Vallverdú.His current research is supported as part of the following projects: GEHUCT and ICREA Acadèmia.