The self-assembling brain : how neural networks grow smarter / Peter Robin Hiesinger.
By: Hiesinger, Peter Robin [author.].
Publisher: Princeton : Princeton University Press, ©2021Description: 364 p.Content type: text Media type: unmediated Carrier type: volumeISBN: 9780691181226.Subject(s): Neural networks (Computer science) | Neural circuitry -- Adaptation | Learning -- Psysiological aspects | Artificial intelligenceGenre/Form: Print books.Summary: "In this book, Peter Robin Hiesinger explores historical and contemporary attempts to understand the information needed to make biological and artificial neural networks. Developmental neurobiologists and computer scientists with an interest in artificial intelligence - driven by the promise and resources of biomedical research on the one hand, and by the promise and advances of computer technology on the other - are trying to understand the fundamental principles that guide the generation of an intelligent system. Yet, though researchers in these disciplines share a common interest, their perspectives and approaches are often quite different. The book makes the case that "the information problem" underlies both fields, driving the questions that are driving forward the frontiers, and aims to encourage cross-disciplinary communication and understanding, to help both fields make progress. The questions that challenge researchers in these fields include the following. How does genetic information unfold during the years-long process of human brain development, and can this be a short-cut to create human-level artificial intelligence? Is the biological brain just messy hardware that can be improved upon by running learning algorithms in computers? Can artificial intelligence bypass evolutionary programming of "grown" networks? These questions are tightly linked, and answering them requires an understanding of how information unfolds algorithmically to generate functional neural networks. Via a series of closely linked "discussions" (fictional dialogues between researchers in different disciplines) and pedagogical "seminars," the author explores the different challenges facing researchers working on neural networks, their different perspectives and approaches, as well as the common ground and understanding to be found amongst those sharing an interest in the development of biological brains and artificial intelligent systems"--Current location | Call number | Status | Date due | Barcode | Item holds |
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On Shelf | QA76.87 .H53 2021 (Browse shelf) | Available | AU00000000017664 |
Browsing Alfaisal University Shelves , Shelving location: On Shelf Close shelf browser
QA76.87 .A44 2018 Neural networks and deep learning : a textbook / | QA76.87 .G49 2018 How smart machines think / | QA76.87 .H39 2009 Neural networks and learning machines / | QA76.87 .H53 2021 The self-assembling brain : how neural networks grow smarter / | QA76.87 .M447 2021 Machine learning with neural networks : an introduction for scientists and engineers / | QA76.87 .R37 2016 Make your own neural network : a gentle journey through the mathematics of neural networks, and making your own using the Python computer language / | QA76.87 .T73 2019 Grokking deep learning / |
Includes bibliographical references and index.
"In this book, Peter Robin Hiesinger explores historical and contemporary attempts to understand the information needed to make biological and artificial neural networks. Developmental neurobiologists and computer scientists with an interest in artificial intelligence - driven by the promise and resources of biomedical research on the one hand, and by the promise and advances of computer technology on the other - are trying to understand the fundamental principles that guide the generation of an intelligent system. Yet, though researchers in these disciplines share a common interest, their perspectives and approaches are often quite different. The book makes the case that "the information problem" underlies both fields, driving the questions that are driving forward the frontiers, and aims to encourage cross-disciplinary communication and understanding, to help both fields make progress. The questions that challenge researchers in these fields include the following. How does genetic information unfold during the years-long process of human brain development, and can this be a short-cut to create human-level artificial intelligence? Is the biological brain just messy hardware that can be improved upon by running learning algorithms in computers? Can artificial intelligence bypass evolutionary programming of "grown" networks? These questions are tightly linked, and answering them requires an understanding of how information unfolds algorithmically to generate functional neural networks. Via a series of closely linked "discussions" (fictional dialogues between researchers in different disciplines) and pedagogical "seminars," the author explores the different challenges facing researchers working on neural networks, their different perspectives and approaches, as well as the common ground and understanding to be found amongst those sharing an interest in the development of biological brains and artificial intelligent systems"--