The Unseen Theft of America’s Literary History 

This is a microcosm of the danger facing American archives. Because almost nothing is catalogued at the item-level, most of the unique material housed in these most important of repositories is particularly vulnerable to theft. When someone like Breithaupt steals a book, even a very old book, there is a catalog record that tells us it is missing—and likely some kind of duplicate copy somewhere else in the world. But when he steals a letter from Flannery O’Connor to John Crowe Ransom—unless that letter has been photocopied by another person—it basically ceases to exist. Not only do we not have the information in it, but we don’t even know that we don’t have the information in it.

Source: The Unseen Theft of America’s Literary History ‹ Literary Hub

What will become of empathy in a world of smart machines? 

But can machines be expected to be fully empathetic? Signs point to no. It is relatively easy to create a learning brain but we don’t yet know how to create a heart or a soul. In a recent talk at the New Yorker festival MIT Media Lab director Joi Ito asserted that “humans are really good at things computers are not.”

Source: What will become of empathy in a world of smart machines? | Media Network | The Guardian

The Chaotic Wisdom of Wikipedia

But what is genuinely most fascinating, at least to me […] is the strange way it lets you write encyclopedia pages—the structures that have built up since its founding in 2001. The way that Wikipedia is composed is a good example of what happens when you build something so incredibly simple that anyone can use it, and then everyone does.

Source: The Chaotic Wisdom of Wikipedia Paragraphs | The New Republic

The problem with our data-driven world 

In many fields of research right now, scientists collect data until they see a pattern that appears statistically significant, and then they use that tightly selected data to publish a paper. Critics have come to call this p-hacking, and the practice uses a quiver of little methodological tricks that can inflate the statistical significance of a finding. As enumerated by one research group, the tricks can include:

  • “conducting analyses midway through experiments to decide whether to continue collecting data,”
  • “recording many response variables and deciding which to report postanalysis,”
  • “deciding whether to include or drop outliers postanalyses,”
  • “excluding, combining, or splitting treatment groups postanalysis,”
  • “including or excluding covariates postanalysis,”
  • “and stopping data exploration if an analysis yields a significant p-value.”

Add it all up, and you have a significant problem in the way our society produces knowledge.

Source: The problem with our data-driven world | Fusion

A look at the news and events happening in the Libraries at Waubonsee Community College