Description
Discover 'The Self-Assembling Brain,' a groundbreaking exploration that intertwines neurobiology and artificial intelligence, revealing the intricate process of brain development. This insightful book dives deep into how neural networks evolve into fully functional brains by bridging the gap between nature and technology. Written by esteemed scientist Peter Robin Hiesinger, this book is essential for anyone passionate about neuroscience, cognitive science, and AI research. It tackles 'the information problem,' pivotal in both fields, and challenges readers to ponder: How does genetic information guide human brain development, and can artificial intelligence replicate this in shortened timeframes? Throughout engaging fictional discussions and in-depth seminars, Hiesinger navigates the perspectives of leading researchers, offering a rich tapestry of knowledge that highlights both the shared goals and distinct methodologies in the study of biological brains and AI systems. Published by Princeton University Press in 2021, this brand-new trade binding book (ISBN: 9780691181226) is a must-read for scholars and enthusiasts alike. Note: Shipping for this item is free. Please allow up to 6 weeks for delivery. Once your order is placed, it cannot be cancelled.
Note: Shipping for this item is free. Please allow up to 6 weeks for delivery. Once your order is placed, it cannot be cancelled.
Condition: BRAND NEW
ISBN: 9780691181226
Format: Trade binding
Year: 2021
Publisher: Princeton University Press
Description:
What neurobiology and artificial intelligence tell us about how the brain builds itself
How does a neural network become a brain? While neurobiologists investigate how nature accomplishes this feat, computer scientists interested in artificial intelligence strive to achieve this through technology. The Self-Assembling Brain tells the stories of both fields, exploring the historical and modern approaches taken by the scientists pursuing answers to the quandary: What information is necessary to make an intelligent neural network?
As Peter Robin Hiesinger argues, 'the information problem' underlies both fields, motivating the questions driving forward the frontiers of research. How does genetic information unfold during the years-long process of human brain development — and is there a quicker path to creating human-level artificial intelligence? Is the biological brain just messy hardware, which scientists can improve upon by running learning algorithms on computers? Can AI bypass the evolutionary programming of 'grown' networks? Through a series of fictional discussions between researchers across disciplines, complemented by in-depth seminars, Hiesinger explores these tightly linked questions, highlighting the challenges facing scientists, their different disciplinary perspectives and approaches, as well as the common ground shared by those interested in the development of biological brains and AI systems. In the end, Hiesinger contends that the information content of biological and artificial neural networks must unfold in an algorithmic process requiring time and energy. There is no geno
Note: Shipping for this item is free. Please allow up to 6 weeks for delivery. Once your order is placed, it cannot be cancelled.
Condition: BRAND NEW
ISBN: 9780691181226
Format: Trade binding
Year: 2021
Publisher: Princeton University Press
Description:
What neurobiology and artificial intelligence tell us about how the brain builds itself
How does a neural network become a brain? While neurobiologists investigate how nature accomplishes this feat, computer scientists interested in artificial intelligence strive to achieve this through technology. The Self-Assembling Brain tells the stories of both fields, exploring the historical and modern approaches taken by the scientists pursuing answers to the quandary: What information is necessary to make an intelligent neural network?
As Peter Robin Hiesinger argues, 'the information problem' underlies both fields, motivating the questions driving forward the frontiers of research. How does genetic information unfold during the years-long process of human brain development — and is there a quicker path to creating human-level artificial intelligence? Is the biological brain just messy hardware, which scientists can improve upon by running learning algorithms on computers? Can AI bypass the evolutionary programming of 'grown' networks? Through a series of fictional discussions between researchers across disciplines, complemented by in-depth seminars, Hiesinger explores these tightly linked questions, highlighting the challenges facing scientists, their different disciplinary perspectives and approaches, as well as the common ground shared by those interested in the development of biological brains and AI systems. In the end, Hiesinger contends that the information content of biological and artificial neural networks must unfold in an algorithmic process requiring time and energy. There is no geno