Despite being an East-coaster, I'm a member of the Long Now Foundation, which--when I'm asked to describe it--I usually say is like TED, but with a long term view and way better substance. The Long Now gives regular talks, and then puts those talks up in video and audio form for others, who couldn't be in attendance. I subscribe to the podcast in iTunes, and listen to it--along with other podcasts--on my way to and from work.

A few months ago, Brian Christian was the guest speaker, and gave a talk centered around the subject matter of his latest book: Algorithms to Live By. The talk was fascinating, and contained a nice mixture of computer science, statistics, and humor to win the crowd over, and Christian managed to do so without coming across as too "pop science."

I purchased the book that same week, and between juggling work responsibilities and twins, managed to carve out about an hour each night to read through it. There were chapters that held my interest, and chapters that didn't, but overall the book was a fantastic mix of how various computer science problems are also real work problems, and algorithms that solve one can be applied to the other as well.

The first thing that catches you in the book is the discussion of optimal stopping, and how given a decision that needs to be made, you should begin making your choice after 37% of the options have been mulled over, assuming any of the next decisions/options are better than the ones that came before. This is illustrated with the secretary problem, and you can see why the authors led with this example not just in the book, but also in the Long Now talk. It seems both crazy and fascinating to have a difficult decision boiled down to such a hard percentage. The authors then go over different variations of the problem, and show how slight alteration can bring the best outcome.

The authors (Christian and Tom Griffiths) then follow this up with a rapid succession of entertaining problems such as exploit/explore to determine whether you should go with something that you know, or try something new, as well as chapters on sorting, caching, and scheduling, giving messy desk people hope by showing that a stack of files on a desk where something searched for is retrieved and then placed on top of the pile will eventually result in the most optimized sorting methodology for the job, and reminding older, forgetful people that accumulation of knowledge can result in greater time to sift and retrieve that information, renaming so-called brain farts to caching misses.

The chapter on Bayes' rule is where things start to get a little bogged down, but only in the beginning. Eventually, the chapter turns into an explanation on forecasting, showing which various predictive methodologies should be used for which various distributions--even equating the Erlang distribution to politics.

The back half of the book isn't as tight or as entertaining as the parts that came before it, but overfitting was a great read to be perusing while Nate Silver was being hammered for his polling methodology in the most recent election, and the chapter on networking gave a great, easy-to-read introduction to how information networks differ from telephony. The authors then conclude the book with game theory, discussing the tragedy of the commons, and how, as a society, we could pursue better options in order to ensure mass participation in important initiatives.

As somebody who studies and works in computer science and mathematics, I can say that casual readers will likely get lost in some sections, but powering through or re-reading will get you on to the more entertaining sections. This is a great book that works as a science popularizer without injecting fluffy prose/concepts or dumbing the material down.