Tom Chatfield, writer based in the U.K. and the author of Netymology: A Linguistic Celebration of the Digital World reports, "The stories behind the digital age’s most iconic terms show the human side of technology."
It’s a little-known fact that part of Wikipedia’s name comes from a bus in Hawaii.
In 1995, six years before the famous online encyclopedia launched, a computer programmer named Ward Cunningham was at Honolulu International Airport on his first visit to the islands. He was in the middle of developing a new kind of website to help software designers collaborate, one that users themselves could rapidly edit from a web browser. It was a striking innovation at the time. But what to call it?
“I wanted an unusual word to name for what was an unusual technology,” Cunningham told a curious lexicographer in 2003. “I learned the word wiki … when I was directed to the airport shuttle, called the Wiki Wiki Bus.”
Wiki means quick, and Hawaiian words are doubled for emphasis: the very quick bus. With that, Cunningham’s software had the distinctive sound he was looking for: WikiWikiWeb.
Wikipedia, whose development Cunningham wasn’t involved with, is one among countless websites based on his work. The second half of its name comes from the word encyclopedia, with pedia being the Greek term for knowledge: “quick knowledge.” Yet now the site is so successful that its fame has eclipsed its origins—along with the fact that a chance visit to an island gifted the digital age one of its most iconic terms.
I love delving into the origins of new words—especially around technology. In a digital age, technology can feel like a natural order of things, arising for its own reasons. Yet every technology is embedded in a particular history and moment. For me, etymology emphasizes the contingency of things I might otherwise take for granted. Without a sense of these all-too-human stories, I’m unable to see our creations for what they really are: marvelous, imperfect extensions of human will, enmeshed within all manner of biases and unintended consequences.
I give talks about technology to teenagers, and often use Wikipedia as a prompt for discussion. Let’s find and improve an article to make Wikipedia better, I suggest, and in the process, think about what “better” means. My audience’s reaction is almost always the same. What do I mean by improving an article? Aren’t they all written by experts? No, I say. That’s the whole point of a wiki: The users themselves write it, which means no page is ever the last word. There are no final answers, and no ownership beyond the community itself...
Similarly, to speak about technology is to assume: It demands shared notions of sense and usage. Yet there are some terms that deserve more skepticism than most. Sixty years ago, a group of scientists drew up a conference agenda aimed at predicting and shaping the future—at establishing a field they believed would transform the world. Their mission was to use the young science of digital computation to recreate and exceed the workings of the human mind. Their chosen title? The Dartmouth Summer Research Project on Artificial Intelligence.
The assumptions of the Dartmouth Conference, set out in a 1955 proposal, were explicitly immodest: “[T]he study is to proceed on the basis of the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it.” Yet today, the very word “intelligence” continues to lie somewhere between a millstone and a straw man for this field. From self-driving vehicles to facial recognition, from mastery of Chess and Go to translation based on processing billions of samples, smarter and smarter automation is a source of anxious fascination. Yet the very words that populate each headline take us further away from seeing machines as they are—not so much a mirror of human intellect as something utterly unlike us, and all the more potent for this.
As Alan Turing himself put it in his 1950 paper on computing machinery and intelligence, “we can only see a short distance ahead, but we can see plenty there that needs to be done.” If we are to face the future honestly, we need both a clear sense of where we are coming from—and an accurate description of what is unfolding under our noses. AI, such as it is, sprawls across a host of emerging disciplines for which more precise labels exist: machine learning, symbolic systems, big data, supervised learning, neural networks. Yet a 60-year-old analogy fossilized in words obfuscates the debate around most of these developments—while feeding unhelpful fantasies in place of practical knowledge.
Source: The Atlantic
Source: The Atlantic