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.

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. The contributions were originally presented at the 1st Barcelona conference of “Philosophy of Public Health” (5th – 7th May 2016). This book is intended for researchers interested in public health and the contemporary debates surrounding it.

Why AI does not include gender in its agenda? The role of gender in AI, both as part of the community of agents creating such technologies, as well as part of the contents processed by such technologies is, by far, conflictive. Women have been, again, obliterated by this fundamental revolution of our century.  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. The multi-disciplinarity of the contributors, which uses plain language to show the current situation in this field, is a fundamental aspect of the value of this book. Any reader with a genuine interest in the present and future of AI should read it.

This book analyzes the origins of statistical thinking as well as its related philosophical questions, such as causality, determinism or chance. Bayesian and frequentist approaches are subjected to a historical, cognitive and epistemological analysis, making it possible to not only compare the two competing theories, but to also find a potential solution. The work pursues a naturalistic approach, proceeding from the existence of numerosity in natural environments to the existence of contemporary formulas and methodologies to heuristic pragmatism, a concept introduced in the book’s final section. This monograph will be of interest to philosophers and historians of science and students in related fields. 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.