Iteration Famous Quotes

1. The classics are those books about which you usually hear people saying: ‘I’m rereading…’, never ‘I’m reading…’ At least this is the case with those people whom one presumes are ‘well read‘; it does not apply to the young, since they are at an age when their contact with the world, and with the classics which are part of that world, is important precisely because it is their first such contact. The iterative prefix ‘re-’ in front of the verb ‘read’ can represent a small act of hypocrisy on the part of people ashamed to admit they have not read a famous book. To reassure them, all one need do is to point out that however wide-ranging any person’s formative reading may be, there will always be an enormous number of fundamental works that one has not read.

Correlation is enough,” 2 then-Wired editor in chief Chris Anderson famously declared in 2008. We can, he implied, solve innovation problems by the sheer brute force of the data deluge. Ever since Michael Lewis chronicled the Oakland A’s unlikely success in Moneyball (who knew on-base percentage was a better indicator of offensive success than batting averages?), organizations have been trying to find the Moneyball equivalent of customer data that will lead to innovation success. Yet few have. Innovation processes in many companies are structured and disciplined, and the talent applying them is highly skilled. There are careful stage-gates, rapid iterations, and checks and balances built into most organizations’ innovation processes. Risks are carefully calculated and mitigated. Principles like six-sigma have pervaded innovation process design so we now have precise measurements and strict requirements for new products to meet at each stage of their development. From the outside, it looks like companies have mastered an awfully precise, scientific process. But for most of them, innovation is still painfully hit or miss. And worst of all, all this activity gives the illusion of progress, without actually causing it. Companies are spending exponentially more to achieve only modest incremental innovations while completely missing the mark on the breakthrough innovations critical to long-term, sustainable growth. As Yogi Berra famously observed: “We’re lost, but we’re making good time!” What’s gone so wrong? Here is the fundamental problem: the masses and masses of data that companies accumulate are not organized in a way that enables them to reliably predict which ideas will succeed. Instead the data is along the lines of “this customer looks like that one,” “this product has similar performance attributes as that one,” and “these people behaved the same way in the past,” or “68 percent of customers say they prefer version A over version B.” None of that data, however, actually tells you why customers make the choices that they do.

the roughly $800 billion in available stimulus, we directed more than $90 billion toward clean energy initiatives across the country. Within a year, an Iowa Maytag plant I’d visited during the campaign that had been shuttered because of the recession was humming again, with workers producing state-of-the-art wind turbines. We funded construction of one of the world’s largest wind farms. We underwrote the development of new battery storage systems and primed the market for electric and hybrid trucks, buses, and cars. We financed programs to make buildings and businesses more energy efficient, and collaborated with Treasury to temporarily convert the existing federal clean energy tax credit into a direct-payments program. Within the Department of Energy, we used Recovery Act money to launch the Advanced Research Projects Agency–Energy (ARPA-E), a high-risk, high-reward research program modeled after DARPA, the famous Defense Department effort launched after Sputnik that helped develop not only advanced weapons systems like stealth technology but also an early iteration of the internet, automated voice activation, and GPS.