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 genome and no blueprint that depicts the final product. The self-assembling brain knows no shortcuts.
Written for readers interested in advances in neuroscience and artificial intelligence, The Self-Assembling Brain looks at how neural networks grow smarter.
'Hiesinger elegantly moves through a variety of topics, ranging from biological development to AI and ending with a discussion of the advances that deep neural networks have brought to the field of brain-machine interfaces.' - Kamila Maria Jwik, Science
'Hiesinger suggests that instead of looking at the brain from an endpoint perspective, we should study how information encoded in the genome is transformed to become the brain as we grow...The Self-Assembling Brain is organized as a series of seminar presentations interspersed with discussions between a robotics engineer, a neuroscientist, a geneticist, and an AI researcher. The thought-provoking conversations help to understand the views and the holes of each field on topics related to the mind, the brain, intelligence, and AI.' - Ben Dickson, TechTalks
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 genome and no blueprint that depicts the final product. The self-assembling brain knows no shortcuts.
Written for readers interested in advances in neuroscience and artificial intelligence, The Self-Assembling Brain looks at how neural networks grow smarter.
'Hiesinger elegantly moves through a variety of topics, ranging from biological development to AI and ending with a discussion of the advances that deep neural networks have brought to the field of brain-machine interfaces.' - Kamila Maria Jwik, Science
'Hiesinger suggests that instead of looking at the brain from an endpoint perspective, we should study how information encoded in the genome is transformed to become the brain as we grow...The Self-Assembling Brain is organized as a series of seminar presentations interspersed with discussions between a robotics engineer, a neuroscientist, a geneticist, and an AI researcher. The thought-provoking conversations help to understand the views and the holes of each field on topics related to the mind, the brain, intelligence, and AI.' - Ben Dickson, TechTalks
A vital reckoning with how we understand the basic categories of cultural expression in the digital era
Digital and social media have transformed how much and how fast we communicate, but they have...
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