The Undeniably Non-Fiction Book Club (Berkeley)

For those who are interested, here is the link to the detailed results from the voting.

Eight years in the making and drawn from newly unearthed material — private papers of Wall Street titans, an unpublished memoir, and previously undisclosed Federal Reserve deliberations — the book moves chronologically from February 1929 toward the October cliff and into the wreckage that followed. The cast is enormous and the storytelling is propulsive: J.P. Morgan partners quietly offering stocks to politicians at below-market rates, National City Bank's Charles Mitchell pitching Americans on buying stocks the way they bought cars and refrigerators, NYSE president Richard Whitney extolling the exchange as a "perfect institution" while embezzling securities to fund his country-estate fox hunting, a lame-duck Herbert Hoover begging FDR to declare the bank holiday he himself refused to call. Champagne yachts, pump-and-dump schemes, suicides, dalliances with Mussolini — it's all here, rendered with the granular reporting that made Too Big to Fail a modern classic.

What makes this a good book club pick — beyond the sheer narrative momentum — is how openly it invites argument. Sorkin explicitly frames 1929 as a warning for our own moment of crypto and AI exuberance, but reviewers have split sharply on whether he actually lands the lesson. The New York Times Book Review called it a fable of greed, corruption, and incompetence to shock the conscience, while faulting Sorkin for an oddly sympathetic posture toward the very bankers whose schemes he chronicles. Others — Doris Kearns Goodwin, The Wall Street Journal, The Economist, Barack Obama's year-end list — have hailed it as one of the best narrative histories in years. Whether you arrive as a finance nerd, a history buff, or someone who's never cracked a book about Wall Street, there's plenty to chew on: the eerie rhyme with today's markets, the question of whether human nature really is the villain or just a convenient alibi, and whether "this time is different" is the most expensive sentence in the English language. Bring your verdict.

This event will be hybrid. I will host the meeting in person at my house in Berkeley which is near the intersection of College Ave and Woolsey St. I will email people the address the Saturday before the meeting.

Here are the Google Meet details:
Google Meet link: https://meet.google.com/zem-xnbw-tuh
Or dial: ‪(US) +1 641-854-0146‬ PIN: ‪637 012 874‬#

For those who are interested, here is the link to the detailed results from the voting.

The Emergent Mind: How Intelligence Arises in People and Machines (2025) is a work of popular science that examines how thought, perception, and decision-making take shape in both human brains and artificial intelligence systems. Its central concept is emergence—the idea that complex systems can arise from the interactions of many simple parts, none of which is individually intelligent. The authors build intuition for this idea using everyday examples, such as water molecules forming a liquid or ants collectively finding an efficient path, before extending it to neural networks: the framework, originally developed as a model of human cognition, that now underpins modern AI systems from chatbots to image generators. The book traces how the same principles that helped scientists understand perception, language, and choice in people came to drive advances in machine intelligence, and it argues that modeling the mind in this way does not reduce human beings to machines. It closes by considering the current limits of AI, which still depends on large amounts of data and lacks human-like goals, and the possibility of more autonomous systems emerging in the future.

The book is co-authored by two cognitive scientists. Jay McClelland is a professor of psychology, computer science, and linguistics at Stanford University, where he directs the Center for Mind, Brain, Computation, and Technology; he was a key figure in reviving neural network modeling in the 1980s, and his work helped lay the groundwork for today's deep learning. Gaurav Suri is an associate professor of psychology at San Francisco State University, a computational neuroscientist and experimental psychologist who studies motivation and decision-making, and a co-author of the novel A Certain Ambiguity. The two met in 2014, and this is their first book together. The Emergent Mind was named a Best Book of the Year by New Scientist and selected by the Next Big Idea Club, and it is written for a general audience without requiring a technical background.

This event will be hybrid. I will host the meeting in person at my house in Berkeley which is near the intersection of College Ave and Woolsey St. I will email people the address the Saturday before the meeting.

Here are the Google Meet details:
Google Meet link: https://meet.google.com/zem-xnbw-tuh
Or dial: ‪(US) +1 641-854-0146‬ PIN: ‪637 012 874‬#