Algorithms to Live By Quotes

“machine-learning tasks suggests that we can make better decisions by deliberately thinking and doing less”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“high-demand items are placed in a different area, more quickly accessible than the rest. That area is Amazon’s cache.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“When balancing favorite experiences and new ones, nothing matters as much as the interval over which we plan to enjoy them.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“Unless we’re willing to spend eons striving for perfection every time we encounter a hitch, hard problems demand that instead of spinning our tires we imagine easier versions and tackle those first. When applied correctly, this is not just wishful thinking, not fantasy or idle daydreaming. It’s one of our best ways of making progress.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“When the future is foggy, it turns out you don’t need a calendar—just a to-do list.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“Living by the wisdom of computer science doesn’t sound so bad after all. And unlike most advice, it’s backed up by proofs.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“Computer scientists would call this a “ping attack” or a “denial of service” attack: give a system an overwhelming number of trivial things to do, and the important things get lost in the chaos.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“There is no such thing as absolute certainty, but there is assurance sufficient for the purposes of human life. —JOHN STUART MILL”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“One of the implicit principles of computer science, as odd as it may sound, is that computation is bad: the underlying directive of any good algorithm is to minimize the labor of thought.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“Meanwhile, the kale market grew by 40% in 2013 alone. The biggest purchaser of kale the year before had been Pizza Hut, which put it in their salad bars—as decoration.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“(As Harvard’s Daniel Gilbert puts it, our future selves often “pay good money to remove the tattoos that we paid good money to get.”)”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“When Charles Darwin was trying to decide whether he should propose to his cousin Emma Wedgwood, he got out a pencil and paper and weighed every possible consequence. In favor of marriage he listed children, companionship, and the “charms of music & female chit-chat.” Against marriage he listed the “terrible loss of time,” lack of freedom to go where he wished, the burden of visiting relatives, the expense and anxiety provoked by children, the concern that “perhaps my wife won’t like London,” and having less money to spend on books. Weighing one column against the other produced a narrow margin of victory, and at the bottom Darwin scrawled, “Marry—Marry—Marry”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“Inspired by the punched railway tickets of the time, an inventor by the name of Herman Hollerith devised a system of punched manila cards to store information, and a machine, which he called the Hollerith Machine, to count and sort them. Hollerith was awarded a patent in 1889, and the government adopted the Hollerith Machine for the 1890 census. No one had ever seen anything like it. Wrote one awestruck observer, “The apparatus works as unerringly as the mills of the Gods, but beats them hollow as to speed.” Another, however, reasoned that the invention was of limited use: “As no one will ever use it but governments, the inventor will not likely get very rich.” This prediction, which Hollerith clipped and saved, would not prove entirely correct. Hollerith’s firm merged with several others in 1911 to become the Computing-Tabulating-Recording Company. A few years later it was renamed—to International Business Machines, or IBM.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“As Trick points out, sports leagues aren’t concerned with determining the rankings as quickly and expeditiously as possible. Instead, sports calendars are explicitly designed to maintain tension throughout the season, something that has rarely been a concern of sorting theory. For”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“As the applicant pool grows, the exact place to draw the line between looking and leaping settles to 37% of the pool, yielding the 37% Rule: look at the first 37% of the applicants,* choosing none, then be ready to leap for anyone better than all those you’ve seen so far.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“By the 2012 presidential election cycle, their company counted among its clients both the Obama re-election campaign and the campaign of Republican challenger Mitt Romney.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“...it's not enough for a problem to have a solution if that problem is intractable.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“Computer science, however, it's a complicated story, but broadly the object of study in mathematics is truth; the object of study in computer science is complexity.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“I love you`, says one lover to the other.
`I love you too`, the other replies.
And both wonder what exactly the other means by that.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“Any yardstick that provides full information on where an applicant stands relative to the population at large will change the solution from the Look-Then-Leap Rule to the Threshold Rule and will dramatically boost your chances of finding the single best applicant in the group.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“Look-Then-Leap Rule: You set a predetermined amount of time for “looking”—that is, exploring your options, gathering data—in which you categorically don’t choose anyone, no matter how impressive. After that point, you enter the “leap” phase, prepared to instantly commit to anyone who outshines the best applicant you saw in the look phase.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“In the late nineteenth century, the American population was growing by 30% every decade, and the number of “subjects of inquiry” in the US Census had gone from just five in 1870 to more than two hundred in 1880. The tabulation of the 1880 census took eight years—just barely finishing by the time the 1890 census began.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“Caching gives us the language to understand what’s happening. We say “brain fart” when we should really say “cache miss.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“A heavily soiled load might take longer to wash but the usual time to dry; a large load might take longer to dry but the usual time to wash. So, Johnson asked, if you have several loads of laundry to do on the same day, what’s the best way to do them? His answer was that you should begin by finding the single step that takes the least amount of time—the load that will wash or dry the quickest.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“We should pity the poor driver. The ideal parking space, as Shoup models it, is one that optimizes a precise balance between the “sticker price” of the space, the time and inconvenience of walking, the time taken seeking the space (which varies wildly with destination, time of day, etc.), and the gas burned in doing so.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“For instance, let’s say the range of offers we’re expecting runs from $400,000 to $500,000. First, if the cost of waiting is trivial, we’re able to be almost infinitely choosy. If the cost of getting another offer is only a dollar, we’ll maximize our earnings by waiting for someone willing to offer us $499,552.79 and not a dime less. If waiting costs $2,000 an offer, we should hold out for an even $480,000. In a slow market where waiting costs $10,000 an offer, we should take anything over $455,279.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“similar to the full-information game.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“What happens when we just hire them if they’re better than the first applicant, and dismiss them if they’re not? This turns out to be the best possible strategy when facing three applicants; using this approach it’s possible, surprisingly, to do just as well in the three-applicant problem as with two, choosing the best applicant exactly half the time.*”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“Instead, the optimal solution takes the form of what we’ll call the Look-Then-Leap Rule: You set a predetermined amount of time for “looking”—that is, exploring your options, gathering data—in which you categorically don’t choose anyone, no matter how impressive. After that point, you enter the “leap” phase, prepared to instantly commit to anyone who outshines the best applicant you saw in the look phase.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“Intuitively, there are a few potential strategies. For instance, making an offer the third time an applicant trumps everyone seen so far—or maybe the fourth time. Or perhaps taking the next best-yet applicant to come along after”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions