100 books like The Master Algorithm

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. 

This book helped me understand why advances in artificial intelligence are going to have a big impact on productivity and economic growth. I loved the analogies to old technologies like electrification of factories, and newer examples like how Team New Zealand used simulations to change racing tactics and boat design.

The book has an important, big idea at its heart. That idea is that AI helps organizations make better predications, and those better predictions allow organizations to be fundamentally redesigned to take advantage of this. This is where the AI productivity revolution comes from.

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!

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.

Yes, it’s a textbook, albeit a particularly well-written one. You may already have it on your shelf, if you’ve taken a programming class or two.

I’m way too old to have used CLRS as a textbook, though! For me, it’s an effectively bottomless collection of neat little ideas—an easy-to-describe problem, then a series of increasingly clever ways to solve that problem. How often do I end up using one of those algorithms? Not very often! But every time I read the description of an algorithm, I get a nugget of pure joy from the “aha” moment when I first understand how it works.

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.

This was my favorite book when I started my career. It talks about how information is processed, in an intelligent way, in the internet age. It acts as a tutorial to teach developers how to code our own ML programs, from online dating services, to document analyzer, and search engine. The author did an excellent job of explaining abstract ML algorithms with clear examples. His coding style in Python reads clearly, which makes the book more beginner-friendly.

Don’t get disappointed when you know this book is more than a decade old. It was a visionary book back in the day and it is still relevant today.

This could be the first stop of your brand new machine learning journey. I personally like how the technical concept is translated into plain English – each chapter starts with a high-level overview of a ML algorithm or methodology, concise and clear, followed by lots of visual examples and real world scenarios. I can guarantee you won’t get lost halfway. The book focuses on getting you introduced to ML with minimal math. But if you want to grasp some more of math, the next book I recommend is waiting for you. 

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.  

I’ve not met Harry, but he seems to have a logical and sensible head on his shoulders. His writing is considered and grounded, which is exactly what you need when discussing the hype that forever seems to surround AI. This book is another look at this topic and finds yet more ways to explain to readers the difference between human intelligence and our algorithmic attempts at intelligence – which are frequently pretty stupid.