A fascinating guided tour of the complex, fast-moving, and influential world of algorithms - what they are, why they’re such powerful predictors of human behavior, and where they’re headed next. Algorithms exert an extraordinary level of influence on our everyday lives - from dating websites and financial trading floors, through to online retailing and internet searches - Google's search algorithm is now a more closely guarded commercial secret than the recipe for Coca-Cola. Algorithms follow a series of instructions to solve a problem and will include a strategy to produce the best outcome possible from the options and permutations available. Used by scientists for many years and applied in a very specialized way, they are now increasingly employed to process the vast amounts of data being generated, in investment banks, in the movie industry where they are used to predict success or failure at the box office, and by social scientists and policy makers.
What if everything in life could be reduced to a simple formula? What if numbers were able to tell us which partners we were best matched with - not just in terms of attractiveness, but for a long-term committed marriage? Or if they could say which films would be the biggest hits at the box office, and what changes could be made to those films to make them even more successful? Or even who is likely to commit certain crimes, and when? This may sound like the world of science fiction, but in fact it is just the tip of the iceberg in a world that is increasingly ruled by complex algorithms and neural networks.
In The Formula, Luke Dormehl takes listeners inside the world of numbers, asking how we came to believe in the all-conquering power of algorithms; introducing the mathematicians, artificial intelligence experts and Silicon Valley entrepreneurs who are shaping this brave new world, and ultimately asking how we survive in an era where numbers can sometimes seem to create as many problems as they solve.
"A persuasive, timely interrogation of one of our age's most dangerous assumptions: that information is the same as understanding, and that everything which counts can be counted." (Tom Chatfield, author of Netymology and How to Thrive in the Digital Age
"This is exactly the type of book we need to be reading as society considers the computerized control of nearly all the systems that affect our lives." (Chris Dannen, Fast Company)
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Not about algorithms. Not an original book.
First of all, this is not a book about algorithms. The author does not spend any space talking about what algorithms are, how they work, or their history. Rather, he merely uses them as a stand-in for technology, listing example after exhausting example of things that computers and technology can do. Algorithms are given credit for (or blamed for) big data, data mining, statistics, social networking, the internet... the list goes on. Many of these things have algorithms in common, but the author spends no time actually breaking down where the algorithm comes in. It's almost a placeword, like magic.
Not to mention the dizzying and frequent transitions in this book between starry-eyed entrancement and hang-wringing despair. It seems that algorithms will both make everything in society perfect or come down and destroy us and our humanity. There is no real thesis, nor much actual real synthesis, as most of the book is simply book-review type summaries of articles and books. It gives algorithms far too much credit for everything. When a new topic arises with some silicon valley startup proclaiming the benefits of new technology, the author will invariably cite some instance where somebody wrote an algorithm wrong and something bad happened, and then make vague intimations about the danger of this technology. At one point he even suggested that cameras in bars would be able to tell whether the patrons had contracted STDs.
A very disappointing book. I was very interested in learning how OKCupid algorithms worked, or how algorithms actually functioned in conjunction with the hardware of Google's driverless cars. Instead these technologies were merely mentioned, followed by a quotation from a Slate article that I had already read.
- Landon Rordam
Algorithms and data mining