100 books like Prediction Machines

A primary reason to love math is because of its usefulness. This book shows two sides of math’s applicability, since it is so ubiquitously used in various algorithms.

On the one hand, such usage can be good, because statistical inferences can make our life easier and enrich it. On the other, when these are not properly designed or monitored, it can lead to catastrophic consequences. Mathematics is a powerful force, as powerful as wind or fire, and needs to be harnessed just as carefully.

Cathy O’Neil’s message in this book spoke deeply to me, reminding me that I need to be always vigilant about the subject I love not being deployed carelessly.  

What sets this book apart for us is its focus on rethinking the very foundations of AI. The author, Russell, thoughtfully examines the concept of intelligence itself, comparing humans and machines and outlining the necessary milestones for reaching superhuman AI. 

He also doesn't shy away from the current dangers of AI misuse, providing some really great examples.

We found the book very hard to put down, and it raised so many ‘What if?” questions for us!

I hate the way people toss around the term, disruption, to describe everything under the sun, neutering the value of the term in describing why disruption matters. Christensen’s book relies on empirical research, not hyperbole, to describe what makes innovations disruptive and why many companies fail to respond to disruptive innovations. I use the book and a series of Harvard Business Review articles co-authored by Christensen in one of my classes as a way to help my students understand the unique challenges of marketing disruptive innovations. Inevitably, the students who resist the concepts the most are the ones who write several years after graduation about how they are seeing Christensen’s concepts playing out in their markets. 

Many writers have discussed the dangers that artificial intelligence and machine learning represent to our livelihoods, and how clever computers and autonomous robots will supplant us all in the workplace. What I like about this book is that it provides an alternative, and very optimistic, view of how these new technologies are being deployed. The authors present a future based on a partnership, in which artificial intelligence-based tools work in tandem with human workers, enhancing what individuals can do in the workplace rather than replacing them.

This book by Steven Blank is a bible for anyone trying to understand how to build lean startups. The classic mistake that most entrepreneurs make is to go build a product soon after they develop a hypothesis about what customers want. By building products before customer discovery (i.e. verify customer needs and a scalable sales model), many products miss the mark and fail. The book explains how a lean start-up can figure out what customers want before proceeding to build products. This emphasis on a customer-centered approach rather than a product-centered approach can be the difference when it comes to finding product-market fit. A must-read for any founder!

This book provides an excellent description of the various kinds of machine learning approaches and asks the question of whether there will be a Master Algorithm, one single (universal) algorithm that learns all kinds of tasks from data. The author, Pedro Domingos, introduces the different approaches to building intelligence and the research tribes exploring them – Symbolists (with its foundations in Philosophy and Logic), Connectionists (foundations in Neuro/Cognitive Science), Evolutionaries (foundations in Evolutionary Biology), Bayesians (statistical foundations), and Analogizers (Psychology). He also introduces some of his own ideas on what the master machine learning algorithm might look like. It’s a really useful primer for those who are not deeply immersed in machine learning but it’s written for readers with at least a basic engineering and computer science background.

The first comprehensive book on new tech and its impacts, following big steps made in AI progress in the early 2010s. The authors bring home the point that we are undergoing a watershed moment as tools no longer substitute merely for physical labor encroach on mental tasks – hence the book’s title.

After centuries of fleeing blue-collar jobs to take refuge in cerebral work, we are being left with nowhere to run. Not only that, but past technology would automate a given task, whereas the looming Artificial Intelligence is bound to intervene in many, many tasks currently handled by humans.

First of all, I love everything Michael Lewis writes. But this might be his most engaging book yet. Told through the lens of Sam Bankman-Fried (SBF), the founder of cryptocurrency exchange FTX, this is an incredible story.

It’s a story that combines genius, brilliance, folly, technology, and crime that left me wondering whether SBF really did something terrible, or just got desperately unlucky. It guided me through Bankman-Fried’s criminal trial, its aftermath, and the bankruptcy of FTX with a part harsh judgment and part huge empathy.

Well, the author is a friend of mine but it’s also on a topic close to my own academic research interests. So it was kind of compulsory reading for me, but it might be the most enjoyable compulsory reading I’ve ever done.

I thought NFTs were just a gimmick to do with internet pictures. But the authors explain, with great examples, that NFTs are best thought of as a new technology for creating digital assets. And better still, providing the true owner with irrefutable proof of ownership of these digital assets.

What I took away was that NFTs are a new technology for tracking property rights. And since property rights have been fundamental to economic development for millennia, this is a topic one can’t afford not to understand.

I first heard about this book on one of my favorite podcasts: Hard Fork. The author is a famous crypto investor at perhaps the world’s leading technology venture capital firm: a16z. The author got a hard time from the hosts, who are both pretty skeptical of crypto. But he handled it so well I knew I had to read the book.

Read Write Own is persuasive in cautioning against dismissing the entire crypto sector due to early failures. The examples used are great: like the Helium project—a kind of crowdsourced “people’s 5G network” that uses cryptocurrency payments as incentives. If you want to hear the “glass is half full” case for crypto, this is a must read.