Anti Wage-Slavery, Pro-Freedom Quotations Of The Week 631-632

Traditionally, economists have argued that while new forms of automation may displace jobs in the short run, over longer periods of time economic growth and job creation have continued to outpace any job-killing technologies. For example, over the past century and a half the shift from being a largely agrarian society to one in which less than 1 percent of the United States labor force is in agriculture is frequently cited as evidence of the economy’s ability to reinvent itself.

That, however, was before machines began to “understand” human language. Rapid progress in natural language processing is beginning to lead to a new wave of automation that promises to transform areas of the economy that have until now been untouched by technological change.

“As designers of tools and products and technologies we should think more about these issues,” said Pattie Maes, a computer scientist at the M.I.T. Media Lab. Not only do designers face ethical issues, she argues, but increasingly as skills that were once exclusively human are simulated by machines, their designers are faced with the challenge of rethinking what it means to be human.
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it is possible, for example, to envision systems that replace not only human experts, but hundreds of thousands of well-paying jobs throughout the economy and around the globe. Virtually any job that now involves answering questions and conducting commercial transactions by telephone will soon be at risk. It is only necessary to consider how quickly A.T.M.’s displaced human bank tellers to have an idea of what could happen.
A Fight to Win the Future: Computers vs. Humans
By JOHN MARKOFF
New York Times
Published: February 14, 2011
AAA

In recent years, for instance, there have been hundreds of studies on the various genes that control the differences in disease risk between men and women. These findings have included everything from the mutations responsible for the increased risk of schizophrenia to the genes underlying hypertension. Ioannidis and his colleagues looked at four hundred and thirty-two of these claims. They quickly discovered that the vast majority had serious flaws. But the most troubling fact emerged when he looked at the test of replication: out of four hundred and thirty-two claims, only a single one was consistently replicable. “This doesn’t mean that none of these claims will turn out to be true,” he says. “But, given that most of them were done badly, I wouldn’t hold my breath.”

According to Ioannidis, the main problem is that too many researchers engage in what he calls “significance chasing,” or finding ways to interpret the data so that it passes the statistical test of significance—the ninety-five-per-cent boundary invented by Ronald Fisher. “The scientists are so eager to pass this magical test that they start playing around with the numbers, trying to find anything that seems worthy,” Ioannidis says. In recent years, Ioannidis has become increasingly blunt about the pervasiveness of the problem. One of his most cited papers has a deliberately provocative title: “Why Most Published Research Findings Are False.”

The problem of selective reporting is rooted in a fundamental cognitive flaw, which is that we like proving ourselves right and hate being wrong. “It feels good to validate a hypothesis,” Ioannidis said. “It feels even better when you’ve got a financial interest in the idea or your career depends upon it.
Annals of Science
The Truth Wears Off
Is there something wrong with the scientific method?
by Jonah Lehrer
The New Yorker