WSJ: “Are you giving it your all? Maybe that’s too much. So many of us were raised in the gospel of hard work and max effort, taught that what we put in was what we got out. Now, some coaches and corporate leaders have a new message. To be at your best, dial it back a bit. Trying to run at top speed will actually lead to slower running times, they say, citing fitness research. Lifting heavy weights until you absolutely can’t anymore won’t spark more muscle gain than stopping a little sooner, one exercise physiologist assured me. The trick—be it in exercise, or anything—is to try for 85%. Aiming for perfection often makes us feel awful, burns us out and backfires. Instead, count the fact that you hit eight out of 10 of your targets this quarter as a win. We don’t need to see our work, health or hobbies as binary objectives, perfected or a total failure.”
NYTimes: “As housing prices have soared in major cities across the United States and throughout much of the developed world, it has become normal for people to move away from the places with the strongest economies and best jobs because those places are unaffordable. Prosperous cities increasingly operate like private clubs, auctioning off a limited number of homes to the highest bidders. Tokyo is different. In the past half century, by investing in transit and allowing development, the city has added more housing units than the total number of units in New York City. It has remained affordable by becoming the world’s largest city. It has become the world’s largest city by remaining affordable.”
WSJ: “We believe that the 35-to-5 victory of the machine in generating exceptional ideas (not to mention the dramatically lower production costs) has substantial implications for how we think about creativity and innovation. First, generative AI has brought a new source of ideas to the world. Not using this source would be a sin. It doesn’t matter if you are working on a pitch for your local business-plan competition or if you are seeking a cure for cancer—every innovator should develop the habit of complementing his or her own ideas with the ones created by technology. Ideation will always have an element of randomness to it, and so we cannot guarantee that your idea will get an A+, but there is no excuse left if you get a C. Second, the bottleneck for the early phases of the innovation process in organizations now shifts from generating ideas to evaluating ideas. Using a large language model, an innovator can produce a spreadsheet articulating hundreds of ideas, which likely include a few blockbusters. This abundance then demands an effective selection mechanism to find the needles in the haystack.”
Musk in the Walter Isaacson book, “calls the framework for problem solving “the algorithm.” In short, Musk urges his employees to: Question every requirement, Delete any part or process you can, Simplify and optimize, Accelerate cycle time, Automate.” [via WSJ]
Economist on how AI can revolutionise science: “Two areas in particular look promising. The first is “literature-based discovery” (LBD), which involves analysing existing scientific literature, using ChatGPT-style language analysis, to look for new hypotheses, connections or ideas that humans may have missed. LBD is showing promise in identifying new experiments to try—and even suggesting potential research collaborators. This could stimulate interdisciplinary work and foster innovation at the boundaries between fields. LBD systems can also identify “blind spots” in a given field, and even predict future discoveries and who will make them. The second area is “robot scientists”, also known as “self-driving labs”. These are robotic systems that use AI to form new hypotheses, based on analysis of existing data and literature, and then test those hypotheses by performing hundreds or thousands of experiments, in fields including systems biology and materials science. Unlike human scientists, robots are less attached to previous results, less driven by bias—and, crucially, easy to replicate. They could scale up experimental research, develop unexpected theories and explore avenues that human investigators might not have considered.” More.
