Alessio Plebe – författare

The Future of the Artificial Mind is about the social and technological challenges posed by the new wave of artificial intelligence, both from a technical and a cognitive perspective. Deep neural networks have brought about tremendous technological improvements. This renaissance in artificial intelligence, after decades of stagnation, has enabled new technologies capable of surpassing human performance, as in the case of visual recognition. The book reviews the key ideas that have enabled these goals to be achieved and their historical origins. The book also considers some of the ethical and social challenges that the future development of artificial intelligence will face. Will humans fall in love with future android dolls? What will artificial sex be like? And what will it be like to travel in cars that will treat us as passengers instead of drivers? But predicting the future appears more magic than science. But when it comes to artificial intelligence, it is a constant temptation. Since it is well known that "the only way to get rid of a temptation is to enjoy it!", the hypothesis considered in the last chapter is that emerging trends point to a near future in which intelligence will be ubiquitous, but it will be difficult to identify its bearer. We may be heading towards an era of widespread intelligence, but an intelligence without accountability.

The Future of the Artificial Mind is about the social and technological challenges posed by the new wave of artificial intelligence, both from a technical and a cognitive perspective. Deep neural networks have brought about tremendous technological improvements. This renaissance in artificial intelligence, after decades of stagnation, has enabled new technologies capable of surpassing human performance, as in the case of visual recognition. The book reviews the key ideas that have enabled these goals to be achieved and their historical origins. The book also considers some of the ethical and social challenges that the future development of artificial intelligence will face. Will humans fall in love with future android dolls? What will artificial sex be like? And what will it be like to travel in cars that will treat us as passengers instead of drivers? But predicting the future appears more magic than science. But when it comes to artificial intelligence, it is a constant temptation. Since it is well known that "the only way to get rid of a temptation is to enjoy it!", the hypothesis considered in the last chapter is that emerging trends point to a near future in which intelligence will be ubiquitous, but it will be difficult to identify its bearer. We may be heading towards an era of widespread intelligence, but an intelligence without accountability.

Chat-GPT, humanoid robotics, and self-driving cars are just a few of the things that are changing our everyday lives. The rapid advancement of AI is eroding one by one all the cornerstones considered unique of human nature: language, consciousness, creativity, and moral responsibility. The book argues that the revolution we are facing is driven by Alan Turing's "vision". This vision rests on the idea that intelligence is not an intrinsic property of human beings, but is a way in which matter is functionally organized and an attribute we are naturally inclined to ascribe to certain entities. For decades we have pretended that this idea does not have the corrosive power that it actually does, perhaps more so than the Copernican and Darwinian revolutions. But now, given the achievements of new forms of computing based on deep learning and predictive coding, the most common intuitions can no longer avoid the dangerous Turing idea.The book is intended for scholars, researchers, and readers intrigued by the intersections across disciplines interested in understanding the philosophical, ethical, and social implications of Artificial Intelligence and its impact on human nature.

This book examines the concept of “ Neurosemantics”, a term currently used in two different senses: the informational meaning of the physical processes in the neural circuits, and semantics in its classical sense, as the meaning of language, explained in terms of neural processes. The book explores this second sense of neurosemantics, yet in doing so, it addresses much of the first meaning as well. Divided into two parts, the book starts with a description and analysis of the mathematics of the brain, including computational units, representational mechanisms and algorithmic principles.  This first part pays special attention to the neural architecture which has been used in developing models of neurosemantics. The second part of the book presents a collection of models, and describes each model reproducing specific aspects of the semantics of language. Some of these models target one of the core problems of semantics, the reference of nouns, and in particular of nouns with a strong perceptual characterization. Others address the semantics of predicates, with a detailed analysis of colour attributes. While this book represents a radical shift from traditional semantics, it still pursues a line of continuity that is based on the idea that meaning can be captured, and explained, by a sort of computation.