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.
©2014 Luke Dormehl (P)2014 Gildan Media LLC
"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)
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.
l'enfer c'est les autres
Algorithms are a systematic set of rules for handling complex processes often using a recursive methodology (the routine calls itself). The author doesn't really define algorithm this way but he mostly appeals to examples that involve pattern recognition or some kind of sorting of subsets into their most common elements and associates the correlations between those subsets.
He gives good examples on the state of algorithms in use today and how they aid us in our decision making (just think Google's search engine). He gives an example how the programmers got it wrong in creating an algorithm for social aid in Colorado. The program thought only homeless people deserved medical coverage for the poor and other such poor interruptions of policy.
The author seems to think that "human intuition" can trump an algorithm. That just seems too naive and his examples in the book were never really convincing. Poor programming of misunderstood policy will lead to bad results, but the algorithm can be improved. A good algorithm can save lives and make better decisions (often with human interaction).
Google knows what I want to search for before I do, and Amazon recommends books better than I can, their algorithms are very good. Humans have there place with their intuitions, but a good tool can be a priceless aid. They're not perfect, but they continually get better. Watson beat the best Jeopardy contestants in the country using its algorithm. As Ken Jennings said "I, for one, welcome our computer overlords" as he answered the final question while losing badly.
A book about Algorithms should be keeping the listener on the edge of his seat. This book did no such thing. There wasn't really one thing in the book that I didn't already know (I lie. Will Smith uses patterns of recent Hollywood Blockbusters to determine his next movie is something I did not know. I don't care for Hollywood Blockbusters and that fact had escaped me).
If you have any interest in Algorithms (and who among us doesn't?), I would recommend one of these three recent Audbile books that I have listen to instead, "Dataclysm", a book on big data, and big data allows for the pattern recognition and sorting that's mentioned in this book; "The Second Machine Age", tells what's really going on with algorithms now and how society is changing because of it; and one of my favorites, "Superintelligence", tells where we will end up because of the recursive algorithm.
My favorite genres are absurdist humor, Sci-fi & modern fantasy, but, as you can see, I'll read just about anything. Don't mind the typos.
Excellent insight into the world of algorithms, what they are and how they are being used to classify, label and identify all of us. A lot more interesting than I thought it would be. Great narration and worth the credit.
The discussion is on the level of literature review -broad and interesting, but not always deep enough. Some of the arguments are simplistic and overstated.
An educator and senior who listens to his books from his phone through his hearing aids.
The Formula is a summarized the many ways "Big Data" is being processed through algorithms to predict, diagnose, and match. The selected illustrations are interesting, but reviewed only superficially.
Retired Political Science professor from a community college. Especially like Legal Thrillers.
Somewhat superficial. Could have been covered in a long TED talk. Some of the examples were interesting.
I enjoyed this book. Algorithms are mathematical formula that can predict what we like, what we want, and what we need.
This book describes how formulas and algorithms affect our every day life. From what we search, to what advertising as we see. Algorithms and formula are increasingly being used in investing, medical, and social applications.
I enjoyed this book.
This book isn't meant to be an in depth study of algorithms, it is merely an explanation of what they are and how they are used currently to quantify our lives. Not the for the hardcore technical person but a great listen for anyone wanting to get a general understanding on the topic.
The highlight of the book was the chapter about online dating. Fantastic.
I cannot say the same for the rest of this book. While the author did seem to understand some of the biases inherent to algorithms, he seemed wholly unaware of the biases in criminology research. His chapter on predicting crime was horrible, truly horrible. His critical thinking ability seemed to have been on hold. In a different section, he wrote about the biases of judges when sentencing but never quite seemed to connect how bias affects predicting criminality. In America (or elsewhere), we don't do a very good job of predicting crime. If we don't know who the criminals are and we base algorithms on our faulty data, then those algorithms are faulty. If we use faulty algorithms to arrest and cage people, the outcome is not crime reduction. Instead it serves only make ourselves feel better. In America, we disproportionately target, arrest, convict, and cage black people. We label and treat them like criminals when, at the same time, we allow many white people to go free, even though they have engaged in the same actions. It is unjust. People like Dormehl contribute to the legitimacy of the awful and non-scientific practice of targeting, labeling, and taking the life (but execution, locking in a cage, or robbing them of an opportunity to seek employment, housing, or education) of a disproportionate number of black people and poor people in general. That is not ok. The worst part is, data about biases in crime prediction and prevention are *easily* found. Every intro to criminology/criminal justice textbook is clear on the problem of measuring crime. To be unaware of these very common problems is simply sloppy research. Dormehl chose to ignore the myriad data available. For that reason, I have to question his critical thinking ability in general. This makes me wonder about all of the research in this book, even the bits I enjoyed.
It made me think about how much of our privacy we may be surrendering by not reading and thinking about privacy statements from corporations like Google, Facebook, Microsoft and Amazon. Windows 10, for example, assigns an "Advertising ID" to each user, and harvests information from calendars, apps (including Bing and Yahoo! searches), emails, text messages, phone calls, contacts and browsing history, as well as device location and usage behaviour around music, alarm settings and internet purchases. What they don't know about you can be supplemented by purchasing demographic information from third parties. Catana (Microsoft's Siri) collects your speech pattern data and sends it to Microsoft. Maybe Catana thinks you sound gay, or have traces of some foreign accent, or anything else that a human expert might be able to infer from speech patterns. All of this data collection enables Microsoft and Google to create a pretty complete picture of a person (including age, gender, sexual preference), and to tailor search results accordingly. Typing in a Google search isn't exactly the same as going to the library and collecting your own information - you see what Google or Microsoft thinks you should see.
He sounds more intelligent than the voice in my head.
It's not really a technical book on algorithms; it's more of a social commentary on the brave new world we're heading into.
Report Inappropriate Content