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?

Welcome to AI book reviews, a series of posts that explore the latest literature on artificial intelligence. The history of artificial intelligence is filled with theories and attempts to study and replicate the workings and structure of the brain. Symbolic AI systems tried to copy the brain's behavior through rule-based modules. Deep neural networks are designed after the neural activation patterns and wiring of the brain. But one idea that hasn't gotten enough attention from the AI community is how the brain creates itself, argues Peter Robin Hiesinger, Professor of Neurobiology at the Free University of Berlin (Freie Universität Berlin).