Grace Lindsay is a computational neuroscientist currently based at University College, London. She completed her PhD at the Centre for Theoretical Neuroscience at Columbia University, where her research focused on building mathematical models of how the brain controls its own sensory processing. Before that, she earned a bachelor’s degree in Neuroscience from the University of Pittsburgh and received a research fellowship to study at the Bernstein Center for Computational Neuroscience in Freiburg, Germany. She was awarded a Google PhD Fellowship in Computational Neuroscience in 2016 and has spoken at several international conferences. She is also the producer and co-host of Unsupervised Thinking , a podcast covering topics in neuroscience and artificial intelligence. Her first book is Models of the Mind . Why science? I started my undergraduate degree as a neuroscience and philosophy double major and I think what drew me to both topics was the idea that if we just think rigorously enou
This is very much a book of two halves, the first a history of the development of probability theory and the second examples of where probability goes wrong and an attempt to explain why we're so bad intuitively at estimating chances. Steven Tijms has a rather old-fashioned writing style, which made me think this was a reprint of a book from the 1960s until I hit an entry on Covid-19. This means that the historical section is sometimes a little dry - but strangely, for me, this first half was by far the best part. The main reason for my preference was originality. Although the 'where probability goes wrong' section is arguably the reason books on probability are so much fun, whether it's dealing with the gambler's or prosecutor's fallacy - or the inevitably Monty Hall problem - Tijms was mostly treading very familiar ground here. However, there were parts of the the history section covering aspects that I've rarely seen before in a popular mathematics text.