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Computing

by Schmidt and Cohen

An exploration of the ways in which technology and diplomacy will intersect. 

It deals with the future of war and terrorism. So far, it has been easy to use scare tactics and Web charisma to mobilize acolytes. But a new accountability is coming, and a wired, well-informed public will be able to tell the difference between stardom and wisdom. “The consequence of having more citizens informed and connected is that they’ll be as critical and discerning about rebels as they are about the government,” the authors write.

This book says why leaders, will need much more elaborate planning skills than they ever had before. “States will long for the days when they only had to think about foreign and domestic policies in the physical world”. Future politicians will have to devise policies for both the real and virtual worlds, and those policies will not necessarily be consistent with each other. There is evidence for the authors’ claim that cyberwarfare and drone strikes will overshadow traditional combat.

It’s no longer true, the authors say, that everyone will be famous for 15 minutes. Thanks to the unforgiving nature of the Internet, everyone will be famous forever. “It’s only a question,” they say, “of how many people are paying attention, and why.”

The authors say “that China’s future will not be bright”. Also they predict that there will be many  political candidates with big personalities who become briefly popular but cannot withstand tough scrutiny. They advise political consultants to map the brain functions of candidates for a scientific assessment of how well they handle stress and temptation. When a politician makes it past that kind of screening, we will have reached the digital age.


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Big Data: A Revolution That Will Transform How We Live, Work and Think
by Viktor Mayer-Schönberger and Kenneth Cukier 

An overview of the promises, advancements, issues and implications of the big data revolution.

The ability to easily and cheaply capture and store massive amounts of data in a way that was impossible before, means we are no longer constrained to statistical methods of sampling or estimation in order to extract meaning from data.

Instead, collecting a complete data set means that we can now analyze the dataset in its entirety, as well. Simply put, analyses from here on out must focus on the subject N=all, rather than attempting to guess at a population or hope for a representative subset based on random sampling of data. “Big data” means that we can have it all.
As Mayer-Schönberger and Cukier put it, “when we talk about big data, we mean “big” less in absolute than in relative terms: relative to the comprehensive set of data.” Instead of just using bits and pieces of the data, we want to process as much of it as we can, finally seeing the forest despite the trees.
This shift in statistical measurement comes with its own set of problems. The larger a dataset, the more likely it is to have errors, and the less likely analysts are to have time to carefully clean each and every datum point. However, data scientists have found that even massive error-prone datasets are more reliable than pristine but tiny samples. In a messy dataset, the authors write, “any particular reading may be incorrect, but the aggregate of many readings will provide a more comprehensive picture.” Essentially, the messy whole can outperform exact, accurate subsets.
As we make inroads into big data, we make an important shift from results that focus on causation to results concerned only with correlation. Mayer-Schönberger and Cukier state:
“If millions of electronic medical records reveal that cancer sufferers who take a certain combination of aspirin and orange juice see their disease go into remission, then the exact cause for the improvement in health may be less important than the fact that they lived. Likewise, if we can save money by knowing the best time to buy a plane ticket without understanding the method behind airfare madness, that’s good enough.”
This is a radically different approach to problem-solving than many of us are used to. Rather than adhering strictly to the traditional scientific method, big data allows us to work backward, first starting with data collection, then analysis and finally drawing conclusions from whatever patterns may appear.
This shift away from trying to support or disprove a theory cancels out the possibility of researcher bias, but lends itself to a directionless investigation, with results subject to the interests of the analysts exploring the data. Essentially, the only answers that will be found are the ones a researcher chooses to look for.
With their Kindle e-book readers, for example, Amazon.com can tabulate which sections of books are most highlighted, where readers tend to stop reading and which themes prompt the most user engagement. But since these answers don’t do anything for their long-term business goals, the data just sits there. A publishing company, however, given this same information, might use it to tweak advertising, author writing styles and marketing campaigns. In this example, both companies are using the same data, but the ‘answers’ they get from a set of data may be completely different. In the world of big data, the mindset with which researchers approach a dataset can make all of the difference.
Mayer-Schönberger and Cukier cover data ethics, collection techniques and even a shift in our natural thought processes — just some of the highlights in their book. 


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