Fans pick 100 books like Discriminating Data

In these extraordinary lectures, Foucault offers a comprehensive social history of neoliberal theory from the 1920s to 1980 in Germany, France, Britain, and the U.S. While this work does not focus on racism, Foucault critically elaborates neoliberalism’s defining ideas: the attack on the state and dissipation of the commons, the privatization of everything, and the individualizing of all responsibility; financializing all social and individual choice, while redefining social subjectivity as “the Man of Enterprise,” of innovation and self-making. Foucault opened me to seeing how the liberal in “neoliberalism” signals the elevation of individual freedom as a counter to state power and social support. The question this raised for me is how these shifts in social thought suggest new developments in racist expression.

Stuart Hall provided me with a model for mapping the shifting political conjuncture in real time, and the transforming racial dynamics that centrally shaped neoliberalism’s political emergence and cultural expression of the period. He showed how the newly emergent racial politics identified with neoliberalizing societies is increasingly linked to the immigrant, the unbelonging, the supposed rise in local crime as a consequence, and the perceived threat to the traditional culture of their host society. Hall offers the dynamic terms of analysis for these emerging phenomena: the floating signifier of race, the pluralizing of racism, racial panics, the law and order society, articulation of race with class and gender, etc. His work, so formatively brought together here by Paul Gilroy and Ruth Wilson Gilmore, was enormously generative for me in analyzing the formative connections of neoliberalization and the shifting dynamics of racial politics.

Lisa Lowe and I were in sustained conversation as we were composing our respective books. I read earlier drafts of hers as I was writing mine. Her analysis of settler-colonialism, the African slave trade, and trade in Asian goods and peoples in the Caribbean and Americas illustrated for me ways of thinking about the global relations and interactive impacts of the movements of people, culture, and thought. Her focus on how liberal thought shaped and is shaped by these relations helped to surface the coercive and discriminatory practices that made liberal thought possible. This “history of the present” by extension was enormously generative for thinking about the history of the neoliberal present too. 

A central idea of racial neoliberalism is the erasure of concepts referencing race, taking away the very terms by which racism can be identified and critically addressed. This is a condition that, with Obama’s election in 2008, became increasingly widely identified as “the postracial.” I find this edited volume more readily than others to provide trenchant analysis of the complex relations between the condition of the postracial and its rendering of racism less readily identifiable and more challenging to address.

The other books in this series are mostly about the real brain. But artificial intelligence promises us a new enhanced brain. What does the future hold? Terrence Sejnowski is a neuroscientist who was one of the first to realize the potential of AI. Since he has been there from the start, in this book he gives the reader an exciting inside story on the people and the advances that are reshaping our lives.

Early attempts at AI were limited, but once computational power took off big computers running multilayer neural nets began proving that they could defeat humans at the most demanding games, enhance human capabilities such as pattern recognition, text recognition, language translation, and driverless vehicles, and work to obtain rewards, just like a human. While these advances are dramatic, it is well to remember that the networks are built not from representations of real neurons, but rather from… show more.

This is a clever and highly readable guide to the brave new world of algorithms: what they are, how they work, and their strengths and weaknesses. It’s packed with stories and vivid examples, but Dr Fry is a serious mathematician and when it comes to the crunch she is well able to show it with clear and rigorous analysis.

I love gutsy books by outsiders, and Ms. Saxena, as a woman of color working in the Ivy League and the worlds of artificial intelligence and Big Data, is very much an outsider.

That makes her deep knowledge and insights into how AI and Big Data are changing business even that much more interesting. Plus, this is one of the only books I’ve read that explains how artificial intelligence works in a clear, direct way that doesn’t assume the reader already knows about things like machine learning and neural nets. 

If ever a subject deserved the sweeping hand of a highly skilled journalist/historian, it’s generative AI and machine learning. The field is shaped by its founders’ idiosyncratic and fascinating personalities.

NYTimes reporter Cade Metz observed many events first-hand. We read about Go Grandmaster Lee Sedol recovering from losing to Google’s AI by mastering the machine’s logic. We see Geoffrey Hinton flying supine because of his back problems, and the origins of Joy Buolamwini’s famous Gender Shades project.

We get the backstory to the most serious issues: like how well can AI developers be trusted to manage risk? As a journalist-historian myself, I deeply appreciate being immersed in contemporary history. 

I have a deep appreciation for Stuart Russell's book because it brilliantly balances theoretical foundations with practical applications in AI. This book is not just a textbook; it’s a comprehensive guide that covers everything from problem-solving and knowledge representation to machine learning and ethics.

Russell's clear explanations and engaging examples make complex concepts accessible, which resonates with my own passion for demystifying AI for readers. I recommend it to anyone interested in understanding AI's potential and challenges, as it equips you with the knowledge to navigate this rapidly evolving field responsibly and thoughtfully.

Peter Flach’s book on machine learning had a profound impact on me. The book is simple to understand, and highly visual. But beyond that Peter himself is a lovely person who obviously cares about all his students. I believe for getting started in machine learning and wanting to understand the algorithms that power many models, this is a great place to start.

But most importantly it’s a great way to understand the power and gain more intention behind what we are doing.