This book was pretty good. It describes the good and bad aspects of reducing everything to a set of data. Some of it is rather frightening in scope. Sure you've heard of self-driving cars, but would you need to have a non-self-driving car in an emergency of some kind? This book is merely a cursory introduction to algorithms and how they affect our lives. In some ways it could turn into some kind of nightmarish dystopia, but in others it can improve our way of life.

The author presents a lot of examples of human bias getting in the way of reason, while at the same time acknowledging that there are many things that computers and algorithms can't do well. The biggest one could be object recognition. At the same time, it probably won't take too long for us all to be like those characters on Wall-E; fat and unemployed since all of the jobs are done by robots. ( )

I've found that this is not an easy book to review. It is fairly dense with information....so much so that I've found it a bit difficult to draw out the main ideas. I guess the main ideas are that algorithms are being used extensively already ...some of them with remarkable accuracy.......but they are not foolproof and individuals who don't fit the algorithmic profile might be greatly disadvantaged. Steven J Gould, in his book "The Mismeasure of Man" warned about the dangers of converting concepts such as "intelligence" into simplified measures such as IQ. Apparently objective truths can be used to back up human biases rather than exposure genuine insights.
Amazon and Google use their data on previous purchases to decide the sort of things you might like to buy and henceforth that is what you will see. There is an obvious danger that, of course, there might be other things you would like to see but the inputs are being filtered by an algorithm. A lot of algorithmic effort has been devoted to partner matching; finding the perfect date or partner....or the perfect hospital to do your internship etc. Apps have even been developed that will beep when a compatible partner is nearby.
AI is also being used to predict where crimes might be expected to occur and policing resources are focused on those areas. Apparently predictions made by algorithm were 2x as accurate as human experts. They have also been used to profile criminals and predict their likelihood of committing a crime. So, for example, if a person commits an armed robbery at age 14 that is a good predictor of further crime. If they committed the same crime at age 30 that doesn't predict very much. In a sense these are kind of black box systems with very little in the way of real understanding in the system. I must say that I worry about this sort of AI. The fact that a 14 year old committed an armed robbery might relate to their poor environment, exposure to violence and weapons. It probably also relates to the way their brain has developed. (My note: Early exposure to fear and violence shapes the reactions in the brain, and some behaviour patterns are because of genetic differences in their brain.......so if we knew about this brain differences we could probably also make predictions about future criminal activity....but maybe we could also take remedial measures).
There is a company in the UK called Epagogix that purports to be able to predict how much money a film will make ...and it appears to be remarkable accurate. With music, attempts have been made to quantify quality but the pundits have had to fall back on the concept of appeal. Apparently quality in the arts is pretty much a social construct. And a danger of producing a film that appeals to the masses is that you get the lowest common denominator. Lior Shamir has built an automated art critic algorithm and says that at first it will not be possible for algorithms to move forward in a meaningful way rather than just aping what has come before ...but ultimately he "would be very careful in saying there are things that machines can not do".
Nothing, it seems is safe from a few well designed algorithms offering speed efficiency and value for money. A number of experts are predicting that there will be 10-40% fewer lawyers a decade from now as there are today 2012.
As Steven Pinker writes in "The Language Instinct" ..."The main lessons of 35 years of AI research is that the hard problems are easy and the easy problems are hard......as the new generation of intelligent devices appears, it will be the stock analysts and petrochemical engineers and parole board members who are in danger of being replaced by machines. The gardeners, receptionists and cooks are secure...." (Though I'm not even sure about the latter).
Where 5 or 10 years ago people would be happy with any recommendations coming from an algorithm....now they are wanting to know why these recommendations have been made.
Some poor guy was pulled up 80 times in one year as a potential terrorist...based on an algorithm. They certainly don't always get things right and individual rights seem to be overridden in the belief that if the algorithm gets it right 95% of the time then that is good enough and society has to accept the collateral damage.
On the whole a bit of a grab bag of hundreds of examples of AI and Algorithms being used and a warning about a future where our freedoms were being eroded by such algorithms....because they (most times) didn't reflect the real human world exactly but were using a black box approach.
I would have preferred a more structured approach rather than the grab-bag, personally. ( )

Interesting and readable account of how companies and researchers use number crunching to not only predict consumer behaviour but, more ominously, control it. ( )