This article is part of Demystifying AI, a series of posts that (try to) disambiguate the jargon and myths surrounding AI. In the 1980s, Geoffrey Hinton was one of the scientists who invented backpropagation, the algorithm that enables the training of deep neural networks. Backpropagation was key to the success of deep learning and its widespread use today. But Hinton, who is one of the most celebrated artificial intelligence scientists of our time, thinks it is time that we think beyond backpropagation and look for other, more efficient ways to train neural networks. And like many other scientist, he draws inspiration from the human brain.

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).

Despite their overlapping interests, it is rare for developmental neuro biologists to consult artificial intelligence (AI) experts in the course of their research and vice versa. But in his new book, The Self-Assembling Brain, neurobiologist Peter Robin Hiesinger argues that doing so would likely be of great benefit to both parties. In 10 chapters, he describes a series of imagined conversations between four hypothetical individuals--a developmental geneticist, a neuroscientist, a robotics engineer, and an AI researcher--that offer readers insight into the information that is needed both to understand the workings of the brain and to create an artificial system that mimics the brain. These fictional conversations are followed by "seminars" in which the author discusses specific topics in greater detail. 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.