yping, like working at an old-fashioned sewing machine? Tracing the thread of a word is a beautiful counterbalance to digital culture’s often-relentless obsession with the present, sketching how each iteration of software and hardware builds off older ideas and values. Tune into a wireless network, and you’re entering the same verbal space as the wireless telegraph—developed in the 1890s. Then as now, international agreements needed to be established for successful networking, together with regulation, licensing, and the aggressive assertion of trademarked standards. To communicate electronically is to participate in a vast, negotiated consensus, built and maintained on the basis of decades of accumulated assumptions.
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.
Even though we can get trapped in words, today remains an age of extraordinary linguistic fertility—one marked not only by mass literacy, itself a recent historical phenomenon, but by mass participation in written and recorded discourse. Through the screens of billions of cell phones, tablets, laptops, and desktop computers, humanity is saturated with self-expression as never before.
All these words tell and reveal more than we know, if we care to interrogate them. Etymology insists that everything was once new—although born into a negotiation with all that came before. In a small way, the stories behind new words challenge us to think again: to recover the shock and strangeness of each addition to the world.
I don’t believe that knowing the origin of Wikipedia especially helps us understand its success, or teaches us how to use it better. But it does offer a reminder that things weren’t always like they are today—followed, hopefully, by the insight that things won't always be like they are today, either.