100 books like Computers Ltd.

This is a book that is at once a biography, a testament to human genius in the face of imminent danger, and a story of human injustice. Alan Turing had an idea about a ‘universal machine’. A machine, when built at Bletchley Park, allowed the Allies in World War II to crack the German Enigma ciphers. This universal machine laid the foundations for modern computing and all the amazing advances we enjoy today. But at a price for Turing, he fought inner demons about his homosexuality and eventually paid the ultimate price.

I marveled at his genius, cheered his cryptographic successes with each cipher cracked, shouted against the tragedy of his arrest, cried at his untimely death. A death at his own hand at the age of 41. The world lost a genius due to a society’s labelling of homosexuality as a crime.

We still live in this world of… show more.

This book picks up where Evening in the Palace of Reason leaves off, with Bach composing the Musical Offering on a horrible theme from King Frederick.

It explains canons and fugues, and thus helps you understand Bach’s work better, but it then goes on a safari through the intellectual landscape of ideas related to fugues–strange loops, self-similarity, recursion, and of course the guys in the title. It’s not for everyone, but if you like any two of logic, philosophy, or music, give this a try.

This book is not about computing, but it is relevant in an indirect way. I love this book, since it is written in such an engaging style and illustrates with many examples that math is not a dry subject to be practiced only by mathematicians but helps everyone to solve real-world problems. The book shows how important it is to be precise in describing problems and that applying a little mathematical rigor goes a long way in solving them. Ellenberg describes mathematics as the “extension of common sense by other means.” In a similar way, I view computer science as the extension of problem-solving methods (aka “algorithms”) by other means. 

In the ongoing debates over artificial general intelligence (AGI), Judea Pearl is taking a firm stand: He argues that an intelligent robot should be able to reason about causality and that the currently fashionable approaches to AI miss this aspect.

A celebrated AI researcher and a Turing Prize laureate, Pearl has developed an amazingly original approach to this problem. This book is a high-end popular exposition of his approach.

But it’s so much more than that. It’s a history of statistics and its conflicted attitude to causality. It’s a story of heroes (or villains?) in this history. And it’s a scientific autobiography that describes Pearl’s journey. Pearl likes picking fights with the AI community, statisticians, or economists. He’s boastful, provocative, extremely intelligent, and knows how to tell a story.

I always find myself applying algorithmic thinking in my everyday life—it affects the way I put away dishes, navigate to the store, and organize my to-do lists. And I think others could benefit from that mindset.

So, when I read this book, my reaction was "Yes! That's what I want people to know. I just wish I could have said it that well!" The authors (who I know, but didn't know they wrote a book together), did a fantastic job of selecting algorithms with deep human connections. Really! And they explain them just right, without getting too mathematical but while still hitting the key ideas with clarity and accuracy. Fantastic!

In The Innovators, Walter Isaacson reminds us that “innovation occurs when ripe seeds fall on fertile ground.” Like the earth beneath our feet, we stand on stratum upon stratum of technological innovation, each with unique markers of its age.

Sometimes, the right idea, person, or group of people arrives at the right time to sow the seeds of something new. Isaacson explores how this phenomenon, teaching us how the collaboration of many individuals working across time and space helped usher in the first digital age in a highly readable survey of the main players and events.

As we stand on the brink of a second digital age, we would do well to search our history for lessons before stepping into the future.

The software interface for Apple’s innovative Macintosh was largely (and legally) modeled on system software designed at the Palo Alto < California research center of Xerox, an East Coast photocopy company whose stodgy executives failed to realize the value of the coding breakthroughs they had funded and nurtured in the heart of northern California’s computer cauldron. Before anyone at the top of Xerox realized the enormity of their errors, the company had licensed to Steve Jobs and Apple key software technologies that animated the Macintosh revolution in the 1980s. Hiltzik’s richly detailed and readable history, based on scores of interviews, is the best account of the epic failure of an American corporate icon. Apple and Jobs went on to achieve glory while Xerox ultimately became a zombie company, having missed the greatest industrial wave of the past 75 years. 

This book is about the design of artifacts that are used by humans. It discusses, in particular, how specific features of cognitive artifacts can support or impede their effective use. The physical artifacts discussed in this book provide concrete illustrations for some abstract computer science notions such as types. I have used some of the examples successfully in talks about computer science for the general audience. A focus of this book is on representations, which plays an important role in many areas of computer science. If you enjoy the examples discussed in this book and like to think about representations, then you are thinking like a computer scientist. 

This book changed my entire perspective on writing the UI and UX of great software. Even the revised edition is a bit old but still has many valuable lessons to teach. Platt established many of the fundamental principles of writing usable and transparent software, and his book should be read not only by designers, but perhaps especially by programmers.

Richard’s authoritative leading textbook excellently describes the whole field of computer vision. It starts with the sensor, moves to image formation followed by feature extraction and grouping, and then by vision analysis. It’s pragmatic too, with excellent descriptions of applications. And there is a ton of support material. This is a mega textbook describing the whole field of computer vision.