nk 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.
Some contributors to Ward Cunningham’s original wiki are less than complimentary towards Wikipedia precisely because they see it betraying this intention. By virtue of its success, they argue, Wikipedia encourages an illusion of impartiality and permanence. Its pages can become “self-contained, self-appointed ‘truths’” that close down debate—or restrict it to a self-selected editorial caste.
A larger momentum lies behind such worries: the passage of time itself. None of my teenage audience remembers a time before Wikipedia, and Cunningham’s original website is older than every one of them. Like much of the software and hardware in their lives, Wikipedia is simply a part of the landscape—something people inhabit, adapting themselves to its contours.
Digital technology has an uneasy relationship with time. Old formats and platforms fall into disuse fast; newer is by definition better, faster, brighter. Yet decades-old decisions continue to have an influence. If, for example, you want to understand the design of a computer mouse, you need to dive back into a 1965 NASA paper exploring different possible control methods—including a kind of treadle moved with the knees, a "Grafacon" tablet and stylus, a light pen, a joystick. In doing this, you’ll find yourself transported to a time when it was by no means obvious how people might best interact with a computer, or even what interacting with a computer meant. Sample quote from the original paper: “Although the knee control was only primitively developed at the time it was tested, it ranked high in both speed and accuracy, and seems very promising.”
Who would imagine using their knees to control a computer today in parallel to typing, 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 