Hello World: How to Be Human in the Age of the Machine

"It’s about algorithms. Everybody knows algorithms exist, and we more or less know sort of what they’re used for, but most readers don’t have the full picture of how algorithms work and the many, many different fields in which they appear. This is a cleverly structured book. I don’t know whether it was the author’s idea or whether she got good advice from the publisher, but it’s divided into different fields, like medicine and all sorts of other things that you can possibly think of in which algorithms will appear. She explains in incredibly clear language, with very, very good examples, just how pervasive the use of algorithms is. It’s a great book about our time. No, absolutely not. It is incredibly clearly written. It always comes back to real examples. It’s not theoretical, although she does explain the theory of how it works. None of it, though, is beyond the average reader. It’s a completely fascinating read."

"Computer algorithms are playing an increasingly important role in our lives, so should we be afraid? This immensely enjoyable book from the mathematician and broadcaster, Hannah Fry, looks towards the not-too-distant future when machines might rule supreme, making important decisions on our behalf, not just about what movies we might want to watch online or what items to buy, but in healthcare, transport, finance and security. She explores whether this is the kind of future we want. The fact is, the age of AI is coming fast, and we need to be ready for it. This book will help you decide how worried you should be."

"Yes, I picked Hello World because, as you said, Hannah Fry is a very good communicator. She’s a British mathematician, though she’s become more of an author and communicator in her public life. This book, Hello World , is trying to summarise the state of everything related to data, machine learning, and artificial intelligence . If we think more broadly about this idea of using data to understand the world, I specifically picked this fifth book because while the other books, like Factfulness and Thinking in Bets, are about how you, as an individual, can use data to better understand the world, it’s important to acknowledge that the trend in the last few years is very much not about you as an individual. Rather, it’s about computers using data more and more efficiently to understand the world. This has been the trend and I don’t see it stopping anytime soon—quite the contrary. Because of that, it’s very, very important that people at least start understanding how these algorithms and computer models work. Hannah Fry goes through a whole list of examples of how models and algorithms are used. It includes things that can seem trivial, like marketing, or targeted ads, but also other things that are extremely important, like advising a judge on someone’s sentence or on whether they should be released from prison. She goes through the potential problems that can be raised by algorithms—whether they’re technical problems, ethical problems, or political problems—but what I like about the book is that she’s very honest about the opportunities. On this topic of data and machine learning, there have been different waves of books over time. There was a first wave which was singing the praises of these techniques, trying to teach people how to use them, and making lots of promises about how this was going to change the world for the better. Then there was a second wave of books, which I think was needed, but was extremely critical, basically saying, ‘Hey! These algorithms that you’re being told are going to change the world for the better, well, they’re biased, they’re racist and sexist, and we should probably stop using them.’ “More and more data is not necessarily synonymous with better data” What I like about Hannah Fry’s book is that it’s a ‘third wave’ book. She’s very honest about the fact that these algorithms have limitations and biases, but she analyses these limitations in very nuanced ways. In the past, there have been problems and scandals around some of these models. COMPAS is a big one. It’s the name of the computer model used in the US to advise judges on whether someone in jail should be released. COMPAS was heavily criticised in the US media for using an algorithm with outcomes that were biased against Black defendants. Fry explains that it’s true, but she also explains why you can’t have everything. There are many tradeoffs in the way you set up an algorithm, and you cannot reach perfect fairness without sacrificing accuracy in other ways. Get the weekly Five Books newsletter Fry is also very frank about the fact that using algorithms and computer models to advise us can be extremely good. She puts things in context. Assigning a prison sentence in the US, for example, used to be done in an extremely random way, basically down to one judge. This one judge surely can have just as much—if not more—racial prejudice as a computer model. The fact that whether someone spends six weeks, two years, or 10 years in prison would be based purely on one human being’s assessment is surely not something we want to keep. She writes at some point that if she was being arrested and judged for something she did, she would much rather have an algorithm decide her sentence based on something as close as possible to fairness, rather than a ‘random’ judge who might, for example, be sexist and give her an unfair sentence. Hello World is a very interesting book that can teach people a lot about the way things are going to evolve in the future with machine learning and data, without being too optimistic, but also without being too pessimistic. I’m going to cheat and give you two: it would depend on which part of the title they want to focus on. If they want to focus on the ‘understanding the world’ part, I think Factfulness is really the right one. I recommend going to the site of Gapminder —which is the website of the foundation of Hans Rosling—and trying one of these quizzes that tests your understanding of the world. If people find that interesting, or find out they’re really wrong about the world, I think they would find Factfulness extremely insightful and fun to read as well. If people want to focus more on the first part of the title, ‘using data’, then Nate Silver’s book, although it was written 10 years ago, is still probably the best summary of data science, probabilistic thinking, machine learning, and all of that. In many ways, his book is a summary of all of the other books we’ve talked about. So I would recommend The Signal and the Noise to someone if they only wanted to read one book on using data."