Reading Time: 2 minutes

This is the time of year when the newspapers announce their books of the year. Unfortunately, the reviewers often praise each other’s books, or simply state they couldn’t put it down, without explaining why. Things are a bit different here; every one of the books on my list have been reviewed on this site during the past 13 months (bending the rules slightly to allow myself a wider selection).

Weapons of Math Destruction

The Signal and the Noise

Metaphors we Live by

Crossing the Chasm

Algorithms to Live by

The Book of Why?  (a review of this will follow in December).

The winner is Algorithms to Live by?  Why this book?

Because it provides an excellent overview of how AI tools and a machine-based approach can facilitate content creation and dissemination. I won’t get involved in details about what is or is not machine learning, so I’ll simply say a machine-based approach. In other words, the book praises a way of thinking, rather than slavishly following the adoption of one or two algorithms.  Essentially, the authors claim, there are ways in which machines can be used to assist human processes. There are thousands of processes that can benefit from such an approach; all it takes is a reasonable awareness of what machines are good at (such as remembering numbers, and carrying out operations at vast scale) and what humans are good at (such as making subtle judgements and fine distinctions, and reviewing many disparate factors at once).

Nate Silver’s The Signal and the Noise was a close runner-up. It is perhaps the clearest explanation of Bayesian reasoning I’ve yet read, but his almost universal recommendation to put Bayesian into operation in all circumstances leaves out some essential human caveats to take into account (“I’ve deliberately picked some challenging examples – terror attacks, cancer, being cheated on – because I want to demonstrate the breadth of problems to which Bayesian reasoning can be applied”). He’s a wonderfully entertaining writer, but his rather cavalier attitude to things outside probability (such as historical events) to be somewhat alarming.

In contrast, I found Algorithms to Live by provides practical, immediately applicable advice, an informed perspective that enables you to take a decision straight away, without feeling it was a blanket-like adoption of a single approach. Whether it’s matching socks, or filing old correspondence, this book provides straightforward and well-informed answers. Because it doesn’t restrict itself to a single algorithm, but makes it clear there might be several possible computing-inspired approaches to solving a problem, it is a book that inspires, which is as much as I can ask from any book. It’s  a book I would happily put into the hands of someone wanting to start in the area of content enrichment to see what the fuss is all about, and coming away with an informed idea of the possibilities of this technology.