100 books like Hello World

What if you are faced with a problem for which a standard approach doesn’t yet exist? In such a case, you will need to be able to figure out the approach from the first principles. This book will help you learn how to derive insights starting from raw data.

The opening of this book about how public transport systems have been designed to get men where they need to go (to the city center for work) but not women where we often go (all over neighborhoods caring for people) just blew my mind.

I loved how Criado Perez challenges so many things we take for granted – like why you can go out with a client after work and expense your steak and drinks but not the babysitter you have to hire. Her explanations of how the world is basically designed for men helped me understand why the voice control system in my car never seems to understand me and why there’s always a line for the ladies’ room.

It's amazing how our thinking is influenced by a biased statement. This book shows that there is still hope when you look at the real facts.

The author asks a number of questions that require basic knowledge of everyday data that we read a lot about in the press. Questions such as: "How many people in the world are illiterate?" or "How many women are not educated?" are answered incorrectly by politicians, bankers, and scientists, those who determine our policy.

It is confronting to realize that more questions could have been answered correctly by simply guessing.

I should declare an interest here: I present a BBC Radio show that Blastland and Dilnot created. This book was effectively my “how to” manual on the way into the studio that they had vacated. It’s a wise and varied guide to the power and the pitfalls of data, poetically written and full of subtle wisdoms.

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.  

As I write these lines, artificial intelligence (AI) is getting a lot of attention.

This is largely due to ChatGpt recently bursting onto the scene. But even before ChatGpt began making its mark, AI was often in the news. Some have expressed worry that it will take our jobs, others that it will reinforce systemic oppression by making racially or otherwise discriminatory decisions, and some have even voiced concerns that one day a superintelligent AI might pose an existential threat to humanity.

In the midst of all this, what might get lost is what AI is, what it's capable of doing, and what its limitations are. Wenger's book is intended to address all of these questions. It manages to do so in a way which goes into some of the mathematics of AI systems and yet remain accessible to a lay audience.

After laying out the technical aspects of AI,… show more.

AI continues to be further integrated into our professional lives, and this is a great introductory book for those in business or the public sector to consider the ethical implications of using various AI systems.

It nicely introduces key concepts and provides step-by-step instructions for thinking about and having ethical AI standards, processes, and metrics.

Broadly, it is a call for the importance of ethics in decision-making and business processes and does well to offer guidance in this age of AI.

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.

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 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.