Algorithms to Live By Quotes

“[The physiologist] Claude Bernard extended it to the realm of research, saying that one should not injure one person regardless of the benefits that might come to others. However, even avoiding harm requires learning what is harmful; and, in the process of obtaining this information, persons may be exposed to risk of harm.”
― Brian Christian, Algorithms To Live By: The Computer Science of Human Decisions

“The word “algorithm” comes from the name of Persian mathematician al-Khwārizmī, author of a ninth-century book of techniques for doing mathematics by hand. (His book was called al-Jabr wa’l-Muqābala—and the “al-jabr” of the title in turn provides the source of our word “algebra.”) The earliest known mathematical algorithms, however, predate even al-Khwārizmī’s work: a four-thousand-year-old Sumerian clay tablet found near Baghdad describes a scheme for long division.”
― Brian Christian, Algorithms To Live By: The Computer Science of Human Decisions

“They say: Don’t always consider all your options. Don’t necessarily go for the outcome that seems best every time. Make a mess on occasion. Travel light. Let things wait. Trust your instincts and don’t think too long. Relax. Toss a coin. Forgive, but don’t forget. To thine own self be true.”
― Brian Christian, Algorithms To Live By: The Computer Science of Human Decisions

“But the anguish is unnecessary. Mathematically, at least, these are solved problems.”
― Brian Christian, Algorithms To Live By: The Computer Science of Human Decisions

“Giving yourself more time to decide about something does not necessarily mean that you'll make a better decision. But it does guarantee that you'll end up considering more factors, more hypotheticals, more pros and cons, and thus risk overfitting.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“Taylor created a planning office, at the heart of which was a bulletin board displaying the shop's schedule for all to see. The board depicted every machine in the shop. showing the task currently being carried out by that machine and all the task waiting for it. This practice would be built upon by Taylor's colleague Henry Gantt, who in the 1910s developed the Gantt charts that would help organize many of the twentieth century's most ambitious construction projects, from the Hoover Dam to the Interstate Highway System.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“What's more, exploration can be a curse.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“There are many ways to relax a problem, and we’ve seen three of the most important. The first, Constraint Relaxation, simply removes some constraints altogether and makes progress on a looser form of the problem before coming back to reality. The second, Continuous Relaxation, turns discrete or binary choices into continua: when deciding between iced tea and lemonade, first imagine a 50–50 “Arnold Palmer” blend and then round it up or down. The third, Lagrangian Relaxation, turns impossibilities into mere penalties, teaching the art of bending the rules (or breaking them and accepting the consequences)”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“So explore when you will have time to use the resulting knowledge, exploit when you’re ready to cash in.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“The intuitive standard for rational decision making is carefully considering all available options and taking the best one. At first glance, computers look like the paragons of this approach, grinding their way through complex computations for as long as it takes to get perfect answers. But as we've seen, that is an outdated picture of what computers do; it's a luxury afforded by an easy problem. In the hard cases, the best algorithms are all about doing what makes the most sense in the least amount of time, which by no means giving careful consideration to every factor and pursuing every computation to the end. Life is just too complicated for that.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“[N]ot every problem that can be formally articulated has an answer.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“One of the chief goals of design ought to be protecting people from unnecessary tension, friction, and mental labor.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“Computational kindness isn’t just a principle of behavior; it’s also a design principle.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“The problem is that we’re always buffered.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“The big picture is all you should be worrying about in the beginning”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“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

“heuristic that favors simpler answers—with fewer factors, or less computation—offers precisely these “less is more”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“there’s a wisdom to deliberately thinking less”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“your total amount of regret will probably never stop increasing, even if you pick the best possible strategy”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“the math tells us why: the unknown has a chance of being better”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“exploration is gathering information, and exploitation is using the information”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“the number of robberies you should carry out is roughly equal to the chance you get away, divided by the chance you get caught.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“there are two ways you can fail: stopping early and stopping late”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“the crucial dilemma is not which option to pick, but how many options to even consider”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“tackling real-world tasks requires being comfortable with chance, trading off time with accuracy, and using approximations.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“Optimal stopping tells us when to look and when to leap.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“Computer scientists have been working on finding this balance for more than fifty years. They even have a name for it: the explore/exploit tradeoff.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“To get a better sense for these findings, we talked to UC Riverside’s Amnon Rapoport, who has been running optimal stopping experiments in the laboratory for more than forty years.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“We asked Shoup if his research allows him to optimize his own commute, through the Los Angeles traffic to his office at UCLA. Does arguably the world’s top expert on parking have some kind of secret weapon? He does: “I ride my bike.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“Put 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