The Self-Assembling Brain
How Neural Networks Grow Smarter
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Gesprochen von:
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Joel Richards
Über diesen Titel
How does a neural network become a brain? While neurobiologists investigate how nature accomplishes this feat, computer scientists interested in AI 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. How does genetic information unfold during the 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? Hiesinger explores these tightly linked questions, highlighting the challenges facing scientists, their different disciplinary perspectives, and the common ground shared by those interested in the development of biological brains and AI systems. 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 genome and no blueprint that depicts the final product. The self-assembling brain knows no shortcuts.
©2021 Princeton University Press (P)2023 Tantor
Das sagen andere Hörer zu The Self-Assembling Brain
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Gesamt5 out of 5 stars
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Sprecher5 out of 5 stars
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Geschichte5 out of 5 stars
- homeimprover
- 27.08.2023
Intelligence evolves and cannot be engineered
This book compares 'real brains' with current artificial neural network in AI. The dramatic difference is the absence of evolution and development in AI, which uses randomly connected neural networks that are only trained on big data. The author compares this to brains, which grow based on genes and development of very specifically wired connectomes before any training/learning. The result of gene-based development is a type of intelligence that is specific to each animal already prior to any learning/training. So what intelligence do we produce if we cut all of gene-based development out of the network design in AI?
The author approaches this from many angles. The best is the historical perspective - a long comparison of how AI and brain science go where they are with surprising parallels. There are 10 chapters/lectures, but inbetween always discussions between 4 scientists from different fields, like biologists and AI robotics. These discussions are a lot of fun and give an idea about the perspectives out there.
The narrator is American and does a great job in giving each character a voice.
Overall, an amazing book about what intelligence means, how complex systems come to be, and truly interdisciplinary.
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