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121 " In Silicon Valley, for instance, there’s an adage about meetings: “You go to the money, the money doesn’t come to you.” Vendors go to founders, founders go to venture capitalists, venture capitalists go to their limited partners. It’s possible for the individuals to resent the basis of this hierarchy, but not really to contest its verdict. "
― Brian Christian , Algorithms to Live By: The Computer Science of Human Decisions
122 " 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 "
123 " They would randomly assign patients to either ECMO or the conventional treatment until a prespecified number of deaths was observed in one of the groups. Then they would switch all the patients in the study to the more effective treatment of the two. "
124 " [N]ot every problem that can be formally articulated has an answer. "
125 " A 63% failure rate, when following the best possible strategy, is a sobering fact. Even when we act optimally in the secretary problem, we will still fail most of the time—that is, we won’t end up with the single best applicant in the pool. "
126 " discrete optimization”—that is, there’s no smooth continuum among its solutions. The salesman goes either to this town or to that one; you’re either at table five or at table six. There are no shades of gray in between. "
127 " We can instead use the Threshold Rule, where we immediately accept an applicant if she is above a certain percentile. We don’t need to look at an initial group of candidates to set this threshold—but we do, however, need to be keenly aware of how much looking remains available. "
128 " [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. "
129 " If meaning lies even partially in usage, then you subtly alter the language every time you use it. You couldn't leave it intact if you tried. "
― Brian Christian , The Most Human Human: What Talking with Computers Teaches Us About What It Means to Be Alive
130 " While everyone has a unique way to get motivated and stay that way, all athletes thrive on competition, and that means beating someone else, not just setting a personal best … We all work harder, run faster, when we know someone is right on our heels … "
131 " certain flexibility in the 37% Rule: it can be applied to either the number of applicants or the time over which one is searching. "
132 " The Gittins index, then, provides a formal, rigorous justification for preferring the unknown, provided we have some opportunity to exploit the results of what we learn from exploring. The "
133 " the rich get richer,” and indeed the process of “preferential attachment” is one of the surest ways to produce a power-law distribution. The most popular websites are the most likely to get incoming links; the most followed online celebrities are the ones most likely to gain new fans; the most prestigious firms are the ones most likely to attract new clients; the biggest cities are the ones most likely to draw new residents. In every case, a power-law distribution will result. "
134 " Information Cascades: The Tragic Rationality of Bubbles Whenever you find yourself on the side of the majority, it is time to pause and reflect. —MARK TWAIN "
135 " The Gittins index, then, provides a formal, rigorous justification for preferring the unknown, provided we have some opportunity to exploit the results of what we learn from exploring. The old adage tells us that “the grass is always greener on the other side of the fence,” but the math tells us why: the unknown has a chance of being better, even if we actually expect it to be no different, or if it’s just as likely to be worse. The "
136 " In short, the mathematics of self-organizing lists suggests something radical: the big pile of papers on your desk, far from being a guilt-inducing fester of chaos, is actually one of the most well-designed and efficient structures available. What might appear to others to be an unorganized mess is, in fact, a self-organizing mess. "
137 " They said people should have the right to ask for an explanation of algorithmically made decisions. "
― Brian Christian , The Alignment Problem: Machine Learning and Human Values
138 " This approach, called Simulated Annealing, seemed like an intriguing way to map physics onto problem solving. But would it work? The initial reaction among more traditional optimization researchers was that this whole approach just seemed a little too … metaphorical. “I couldn’t convince math people that this messy stuff with temperatures, all this analogy-based stuff, was real,” says Kirkpatrick, “because mathematicians are trained to really distrust intuition. "
139 " Rather than being signs of moral or psychological degeneracy, restlessness and doubtfulness actually turn out to be part of the best strategy for scenarios where second chances are possible. "
140 " Want to calculate the chance your bus is late? The chance your softball team will win? Count the number of times it has happened in the past plus one, then divide by the number of opportunities plus two. And the beauty of Laplace’s Law is that it works equally well whether we have a single data point or millions of them. "