The design, known as the universal Turing machine, became an influential model for computer processing. After a series of revisions by John von Neumann and others, it evolved into the stored-programming technique—a computer that keeps its program instructions as well as its data in memory.
In the history of computing, the Turing machine is usually considered an innovation independent from the Turing test. But they’re connected. General computation entails a machine’s ability to simulate any Turing machine (computer scientists call this feat Turing completeness). A Turing machine, and therefore a computer, is a machine that pretends to be another machine.
Think about the computing systems you use every day. All of them represent attempts to simulate something else. Like how Turing’s original thinking machine strived to pass as a man or woman, a computer tries to pass, in a way, as another thing. As a calculator, for example, or a ledger, or a typewriter, or a telephone, or a camera, or a storefront, or a café.
After a while, successful simulated machines displace and overtake the machines they originally imitated. The word processor is no longer just a simulated typewriter or secretary, but a first-order tool for producing written materials of all kinds. Eventually, if they thrive, simulated machines become just machines.
Today, computation overall is doing this. There’s not much work and play left that computers don’t handle. And so, the computer is splitting from its origins as a means of symbol manipulation for productive and creative ends, and becoming an activity in its own right. Today, people don’t seek out computers in order to get things done; they do the things that let them use computers.
When the use of computers decouples from its ends and becomes a way of life, goals and problems only seem valid when they can be addressed and solved by computational systems. Internet-of-things gadgets offer one example of that new ideal. Another can be found in how Silicon Valley technology companies conceive of their products and services in the first place.
Take abusive behavior on a social networks as an example. Earlier this year, Chris Moody, Twitter’s vice president of data strategy, admitted, “We have had some abuse on the platform.” Moody cited stopping abuse as the company’s first priority, and then added, “But it’s a very, very hard challenge.” To address it, Twitter resolved to deploy IBM’s Watson AI to scan for hate speech. Google has a similar effort. One of its labs has developed Perspective, an “API that uses machine learning to spot abuse and harassment online.”
Sometimes tech firms will make efforts like these a matter of business viability—the search for “scalable” solutions to products and services. When I asked Twitter about Moody’s comments, a spokesperson told me that the company uses a combination of computational and human systems when reviewing safety content, but they couldn’t share many specifics.