The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies Read Free Page B

false, yes or no, or any other symbolic system. In principle, they can do all manner of symbolic work, from math to logic to language. But digital novelists are not yet available, so people still write all the books that appear on fiction bestseller lists. We also haven’t yet computerized the work of entrepreneurs, CEOs, scientists, nurses, restaurant busboys, or many other types of workers. Why not? What is it about their work that makes it harder to digitize than what human computers used to do?
    Computers Are Good at Following Rules . . .
    These are the questions Levy and Murnane tackled in The New Division of Labor , and the answers they came up with made a great deal of sense. The authors put information processing tasks—the foundation of all knowledge work—on a spectrum. At one end are tasks like arithmetic that require only the application of well-understood rules. Since computers are really good at following rules, it follows that they should do arithmetic and similar tasks.
    Levy and Murnane go on to highlight other types of knowledge work that can also be expressed as rules. For example, a person’s credit score is a good general predictor of whether they’ll pay back their mortgage as promised, as is the amount of the mortgage relative to the person’s wealth, income, and other debts. So the decision about whether or not to give someone a mortgage can be effectively boiled down to a rule.
    Expressed in words, a mortgage rule might say, “If a person is requesting a mortgage of amount M and they have a credit score of V or higher, annual income greater than I or total wealth greater than W , and total debt no greater than D , then approve the request.” When expressed in computer code, we call a mortgage rule like this an algorithm . Algorithms are simplifications; they can’t and don’t take everything into account (like a billionaire uncle who has included the applicant in his will and likes to rock-climb without ropes). Algorithms do, however, include the most common and important things, and they generally work quite well at tasks like predicting payback rates. Computers, therefore, can and should be used for mortgage approval. *
    . . . But Lousy at Pattern Recognition
    At the other end of Levy and Murnane’s spectrum, however, lie information processing tasks that cannot be boiled down to rules or algorithms. According to the authors, these are tasks that draw on the human capacity for pattern recognition. Our brains are extraordinarily good at taking in information via our senses and examining it for patterns, but we’re quite bad at describing or figuring out how we’re doing it, especially when a large volume of fast-changing information arrives at a rapid pace. As the philosopher Michael Polanyi famously observed, “We know more than we can tell.” 2 When this is the case, according to Levy and Murnane, tasks can’t be computerized and will remain in the domain of human workers. The authors cite driving a vehicle in traffic as an example of such as task. As they write,
    As the driver makes his left turn against traffic, he confronts a wall of images and sounds generated by oncoming cars, traffic lights, storefronts, billboards, trees, and a traffic policeman. Using his knowledge, he must estimate the size and position of each of these objects and the likelihood that they pose a hazard. . . . The truck driver [has] the schema to recognize what [he is] confronting. But articulating this knowledge and embedding it in software for all but highly structured situations are at present enormously difficult tasks. . . . Computers cannot easily substitute for humans in [jobs like driving].
    So Much for That Distinction
    We were convinced by Levy and Murnane’s arguments when we read The New Division of Labor in 2004. We were further convinced that year by the initial results of the DARPA Grand Challenge for driverless cars.
    DARPA, the Defense Advanced Research Projects Agency, was founded in 1958