Brian Christian Quotes
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Bayes’s Rule tells us that when it comes to making predictions based on limited evidence, few things are as important as having good priors—that is, a sense of the distribution from which we expect that evidence to have come. Good predictions thus begin with having good instincts about when we’re dealing with a normal distribution and when with a power-law distribution. As it turns out, Bayes’s Rule offers us a simple but dramatically different predictive rule of thumb for each.
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Put broadly, the object of study in mathematics is truth; the object of study in computer science is complexity.
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In fact, for any possible drawing of winning tickets in attempts, the expectation is simply the number of wins plus one, divided by the number of attempts plus two: (w+1)⁄(n+2).
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To live in a restless world requires a certain restlessness in oneself. So long as things continue to change, you must never fully cease exploring.
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Even the best strategy sometimes yields bad results—which is why computer scientists take care to distinguish between “process” and “outcome.” If you followed the best possible process, then you’ve done all you can, and you shouldn’t blame yourself if things didn’t go your way.
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What a familiarity with the construction of Turing test bots had begun to show me was that we fail - again and again- to actually be human with other humans, so maddeningly much of the time.
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Some of the biggest challenges faced by computers and human minds alike: how to manage finite space, finite time, limited attention, unknown unknowns, incomplete information, and an unforeseeable future; how to do so with grace and confidence; and how to do so in a community with others who are all simultaneously trying to do the same.
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To try and fail is at least to learn; to fail to try is to suffer the inestimable loss of what might have been.
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One of the chief goals of design ought to be protecting people from unnecessary tension, friction, and mental labor.
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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.
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At first glance it would seem, of course, that no two subjects could possibly be further apart than an underground society of pickup artists and supercomputer chess. What on earth do these two narratives have to do with each other—and what do they have to do with asserting myself as human in the Turing test? The answer is surprising, and it hinges on what chess players call “getting out of book.” We’ll look at what that means in chess and in conversation, how to make it happen, and what the consequences are if you don’t.
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Information, defined intuitively and informally, might be something like 'uncertainty's antidote.' This turns out also to be the formal definition- the amount of information comes from the amount by which something reduces uncertainty...The higher the [information] entropy, the more information there is. It turns out to be a value capable of measuring a startling array of things- from the flip of a coin to a telephone call, to a Joyce novel, to a first date, to last words, to a Turing test...Entropy suggests that we gain the most insight on a question when we take it to the friend, colleague, or mentor of whose reaction and response we're least certain. And it suggests, perhaps, reversing the equation, that if we want to gain the most insight into a person, we should ask the question of qhose answer we're least certain... Pleasantries are low entropy, biased so far that they stop being an earnest inquiry and become ritual. Ritual has its virtues, of course, and I don't quibble with them in the slightest. But if we really want to start fathoming someone, we need to get them speaking in sentences we can't finish.
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Existence without essence is very stressful.
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Unless we have good reason to think otherwise, it seems that our best guide to the future is a mirror image of the past. The nearest thing to clairvoyance is to assume that history repeats itself — backward.
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The satirical Peter Principle, articulated in the 1960s by education professor Laurence J. Peter, states that “every employee tends to rise to his level of incompetence.” The idea is that in a hierarchical organization, anyone doing a job proficiently will be rewarded with a promotion into a new job that may involve more complex and/or different challenges. When the employee finally reaches a role in which they don’t perform well, their march up the ranks will stall, and they will remain in that role for the rest of their...
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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.
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The ancient Egyptian mummification process involved, for instance, preserving all of a person’s organs except the brain—thought to be useless—which they scrambled with hooks into a custard and scooped out through the nose.
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You should be excited to meet new people and try new things—to assume the best about them, in the absence of evidence to the contrary. In the long run, optimism is the best prevention for regret.
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Learning the structure of the world around us and forming lasting social relationships are both lifelong tasks.
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The math shows that you should always keep playing. But if you follow this strategy, you will eventually lose everything. Some problems are better avoided than solved.
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Sorting something that you will never search is a complete waste; searching something you never sorted is merely inefficient.
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Sometimes there’s so much that needs to be said that the literal site disappears, becomes, as in My Dinner with Andre...
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Computational kindness isn’t just a principle of behavior; it’s also a design principle.
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Why not simply allow them unlimited vacation? Anecdotal reports thus far are mixed—but from a game-theoretic perspective, this approach is a nightmare. All employees want, in theory, to take as much vacation as possible. But they also all want to take just slightly less vacation than each other, to be perceived as more loyal, more committed, and more dedicated (hence more promotion-worthy). Everyone looks to the others for a baseline, and will take just slightly less than that. The Nash equilibrium of this game is zero. As the CEO of software company Travis CI, Mathias Meyer, writes, “People will hesitate to take a vacation as they don’t want to seem like that person who’s taking the most vacation days. It’s a race to the bottom.