# Range - David J. Epstein Synced: [[2023_11_30]] 6:03 AM Last Highlighted: [[2020_01_03]]  ## Highlights [[2019_11_26]] (Location 497) > electrical engineer Claude Shannon, who launched the Information Age thanks to a philosophy course he took to fulfill a requirement at the University of Michigan. In it, he was exposed to the work of self-taught nineteenth-century English logician George Boole, who assigned a value of 1 to true statements and 0 to false statements and showed that logic problems could be solved like math equations. It resulted in absolutely nothing of practical importance until seventy years after Boole passed away, when Shannon did a summer internship at AT&T’s Bell Labs research facility. There he recognized that he could combine telephone call-routing technology with Boole’s logic system to encode and transmit any type of information electronically. It was the fundamental insight on which computers rely. “It just happened that no one else was familiar with both those fields at the same time,” Shannon said. [[2019_11_26]] (Location 612) > To use a common metaphor, premodern people miss the forest for the trees; modern people miss the trees for the forest. [[2019_11_26]] (Location 691) > “Even the best universities aren’t developing critical intelligence,” he told me. “They aren’t giving students the tools to analyze the modern world, except in their area of specialization. Their education is too narrow.” [[2019_11_26]] (Location 692) > He does not mean this in the simple sense that every computer science major needs an art history class, but rather that everyone needs habits of mind that allow them to dance across disciplines. Chicago [[2019_11_26]] (Location 704) > They must be taught to think before being taught what to think about. [[2019_11_26]] (Location 709) > Jeannette Wing, a computer science professor at Columbia University and former corporate vice president of Microsoft Research, has pushed broad “computational thinking” as the mental Swiss Army knife. She advocated that it become as fundamental as reading, even for those who will have nothing to do with computer science or programming. “Computational thinking is using abstraction and decomposition when attacking a large complex task,” she wrote. “It is choosing an appropriate representation for a problem.” [[2019_11_26]] (Location 716) > Arnold Toynbee said when he described analyzing the world in an age of technological and social change, “No tool is omnicompetent.” [[2019_11_26]] (Location 1228) > The study conclusion was simple: “training with hints did not produce any lasting learning.” [[2019_11_26]] (Location 1236) [[favorite]] > If that eighth-grade classroom followed a typical academic plan over the course of the year, it is precisely the opposite of what science recommends for durable learning—one topic was probably confined to one week and another to the next. Like a lot of professional development efforts, each particular concept or skill gets a short period of intense focus, and then on to the next thing, never to return. That structure makes intuitive sense, but it forgoes another important desirable difficulty: “spacing,” or distributed practice. [[2019_11_26]] (Location 1253) > The group with more and immediate rehearsal opportunity recalled nearly nothing on the pop quiz. Repetition, it turned out, was less important than struggle. [[2019_11_26]] (Location 1257) > For a given amount of material, learning is most efficient in the long run when it is really inefficient in the short run. If you are doing too well when you test yourself, the simple antidote is to wait longer before practicing the same material again, so that the test will be more difficult when you do. Frustration is not a sign you are not learning, but ease is. [[2019_11_26]] (Location 1369) > The research team recommended that if programs want to impart lasting academic benefits they should focus instead on “open” skills that scaffold later knowledge. Teaching kids to read a little early is not a lasting advantage. Teaching them how to hunt for and connect contextual clues to understand what they read can be. As with all desirable difficulties, the trouble is that a head start comes fast, but deep learning is slow. “The slowest growth,” the researchers wrote, occurs “for the most complex skills.” [[2019_12_05]] (Location 1572) > Netflix came to a similar conclusion for improving its recommendation algorithm. Decoding movies’ traits to figure out what you like was very complex and less accurate than simply analogizing you to many other customers with similar viewing histories. Instead of predicting what you might like, they examine who you are like, and the complexity is captured therein. [[2019_12_07]] (Location 2207) > The most momentous personality changes occur between age eighteen and one’s late twenties, so specializing early is a task of predicting match quality for a person who does not yet exist. [[2019_12_07]] (Location 2267) > “First act and then think.” Ibarra marshaled social psychology to argue persuasively that we are each made up of numerous possibilities. As she put it, “We discover the possibilities by doing, by trying new activities, building new networks, finding new role models.” [[2019_12_07]] (Location 2286) > Rather than expecting an ironclad a priori answer to “Who do I really want to become?,” their work indicated that it is better to be a scientist of yourself, asking smaller questions that can actually be tested—“Which among my various possible selves should I start to explore now? How can I do that?” Be a flirt with your possible selves.* Rather than a grand plan, find experiments that can be undertaken quickly. “Test-and-learn,” Ibarra told me, “not plan-and-implement.” [[2019_12_07]] (Location 2290) > Paul Graham, computer scientist and cofounder of Y Combinator—the start-up funder of Airbnb, Dropbox, Stripe, and Twitch—encapsulated Ibarra’s tenets in a high school graduation speech he wrote, but never delivered: It might seem that nothing would be easier than deciding what you like, but it turns out to be hard, partly because it’s hard to get an accurate picture of most jobs. . . . Most of the work I’ve done in the last ten years didn’t exist when I was in high school. . . . In such a world it’s not a good idea to have fixed plans. And yet every May, speakers all over the country fire up the Standard Graduation Speech, the theme of which is: don’t give up on your dreams. I know what they mean, but this is a bad way to put it, because it implies you’re supposed to be bound by some plan you made early on. The computer world has a name for this: premature optimization. . . .  . . . Instead of working back from a goal, work forward from promising situations. This is what most successful people actually do anyway. In the graduation-speech approach, you decide where you want to be in twenty years, and then ask: what should I do now to get there? I propose instead that you don’t commit to anything in the future, but just look at the options available now, and choose those that will give you the most promising range of options afterward. [[2019_12_07]] (Location 2487) > codirector of the Laboratory for Innovation Science at Harvard, had InnoCentive solvers rate problems on how relevant they were to their own field of specialization, and found that “the further the problem was from the solver’s expertise, the more likely they were to solve it.” [[2019_12_07]] (Location 2494) > For the most intractable problems, “our research shows that a domain-based solution is often inferior,” according to Lakhani. “Big innovation most often happens when an outsider who may be far away from the surface of the problem reframes the problem in a way that unlocks the solution.” [[2019_12_10]] (Location 2658) [[favorite]] > The more information specialists create, the more opportunity exists for curious dilettantes to contribute by merging strands of widely available but disparate information—undiscovered public knowledge, as Don Swanson called it. The larger and more easily accessible the library of human knowledge, the more chances for inquisitive patrons to make connections at the cutting edge. An operation like InnoCentive, which at first blush seems totally counterintuitive, should become even more fruitful as specialization accelerates. It isn’t just the increase in new knowledge that generates opportunities for nonspecialists, though. In a race to the forefront, a lot of useful knowledge is simply left behind to molder. That presents another kind of opportunity for those who want to create and invent but who cannot or simply do not want to work at the cutting edge. They can push forward by looking back; they can excavate old knowledge but wield it in a new way. [[2019_12_10]] (Location 2671) > “leave luck to heaven,” but were more likely a poetic way to say “the company that is allowed to sell hanafuda.” By 1950, there were a hundred workers, and the founder’s twenty-two-year-old great-grandson took over. But trouble was coming. As the 1964 Tokyo Olympics approached, Japanese adults were turning to pachinko for gambling, and a bowling craze swallowed entertainment dollars. In a desperate attempt to diversify a company that had survived [[2019_12_10]] (Location 2807) > Eminent physicist and mathematician Freeman Dyson styled it this way: we need both focused frogs and visionary birds. “Birds fly high in the air and survey broad vistas of mathematics out to the far horizon,” Dyson wrote in 2009. “They delight in concepts that unify our thinking and bring together diverse problems from different parts of the landscape. Frogs live in the mud below and see only the flowers that grow nearby. They delight in the details of particular objects, and they solve problems one at a time.” As a mathematician, Dyson labeled himself a frog, but contended, “It is stupid to claim that birds are better than frogs because they see farther, or that frogs are better than birds because they see deeper.” The world, he wrote, is both broad and deep. “We need birds and frogs working together to explore it.” Dyson’s concern was that science is increasingly overflowing with frogs, trained only in a narrow specialty and unable to change as science itself does. “This is a hazardous situation,” he warned, “for the young people and also for the future of science.” [[2019_12_12]] (Location 2966) > When the National Transportation Safety Board analyzed its database of major flight accidents, it found that 73 percent occurred on a flight crew’s first day working together. [[2019_12_12]] (Location 2972) [[favorite]] > University of Utah professor Abbie Griffin has made it her work to study modern Thomas Edisons—“serial innovators,” she and two colleagues termed them. Their findings about who these people are should sound familiar by now: “high tolerance for ambiguity”; “systems thinkers”; “additional technical knowledge from peripheral domains”; “repurposing what is already available”; “adept at using analogous domains for finding inputs to the invention process”; “ability to connect disparate pieces of information in new ways”; “synthesizing information from many different sources”; “they appear to flit among ideas”; “broad range of interests”; “they read more (and more broadly) than other technologists and have a wider range of outside interests”; “need to learn significantly across multiple domains”; “Serial innovators also need to communicate with various individuals with technical expertise outside of their own domain.” [[2019_12_12]] (Location 3005) > Facing uncertain environments and wicked problems, breadth of experience is invaluable. Facing kind problems, narrow specialization can be remarkably efficient. The problem is that we often expect the hyperspecialist, because of their expertise in a narrow area, to magically be able to extend their skill to wicked problems. [[2020_01_03]] (Location 3609) > “Balancing the Risks of Mindless Conformity and Reckless Deviation.” [[2020_01_03]] (Location 3749) > Individuals who live by historian Arnold Toynbee’s words that “no tool is omnicompetent. There is no such thing as a master-key that will unlock all doors.” Rather than wielding a single tool, they have managed to collect and protect an entire toolshed, and they show the power of range in a hyperspecialized world.