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.