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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When we talk about decision-making, we usually focus just on the immediate payoff of a single decision—and if you treat every decision as if it were your last, then indeed only exploitation makes sense. But over a lifetime, you’re going to make a lot of decisions. And it’s actually rational to emphasize exploration—the new rather than the best, the exciting rather than the safe, the random rather than the considered—for many of those choices, particularly earlier in life.
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From the mid-eighteenth century onward, computers, frequently women, were on the payrolls of corporations, engineering firms, and universities, performing calculations and doing numerical analysis, sometimes with the use of a rudimentary calculator.
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It is this same central personal vision that is crucial for Nietzsche, who goes so far as to say, “Whether this taste was good or bad is less important than one might suppose, if only it was a single taste! It is precisely the central personal vision of Lanier and single taste of Nietzsche that is lacking in most chatbots.
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Indeed, as Peter Whittle recounts, during World War II efforts to solve the question “so sapped the energies and minds of Allied analysts … that the suggestion was made that the problem be dropped over Germany, as the ultimate instrument of intellectual sabotage.
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You can't be both a painter and a musician and master anything. You can't. And live a life.
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What’s more, sports are not, of course, always designed strictly to minimize the number of games. Without remembering this, some aspects of sports scheduling would otherwise seem mysterious to a computer scientist.
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Like most conversations and most chess games, we all start off the same and we all end the same, with a brief moment of difference in between. Fertilization to fertilizer. Ashes to ashes. And we spark across the gap.
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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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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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Thrashing is a very recognizable human state. If you've ever had a moment where you wanted to stop doing everything just to have the chance to write down everything you were supposed to be doing, but couldn't spare the time, you've thrashed.
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What is the process by which someone comes into our life and comes to mean something to us?
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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.
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To see what happens in the real world when an information cascade takes over, and the bidders have almost nothing but one another’s behavior to estimate an item’s value, look no further than Peter A. Lawrence’s developmental biology text The Making of a Fly, which in April 2011 was selling for $23,698,655.93 (plus $3.99 shipping) on Amazon’s third-party marketplace. How and why had this—admittedly respected—book reached a sale price of more than $23 million? It turns out that two of the sellers were setting their prices algorithmically as constant fractions of each other: one was always setting it to 0.99830 times the competitor’s price, while the competitor was automatically setting their own price to 1.27059 times the other’s. Neither seller apparently thought to set any limit on the resulting numbers, and eventually the process spiraled totally out of control.
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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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When the future is foggy, it turns out you don't need a calendar -- just a to-do list.
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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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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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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.
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I am a musician before a writer, and a drawer before a writer. When I lose sight of that, which I do, my work tends to suffer.
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In 2010 and 2015, the FDA released a pair of draft guidance documents on Adaptive Design clinical trials for drugs and medical devices, which suggests—despite a long history of sticking to an option they trust—that they might at last be willing to explore alternatives.
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We go through digital life, in the twenty-first century, with our guards up. All communication is a Turing test. All communication is suspect.
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Maybe people are finally tiring of watered down grunge rock on the radio.
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Service works by the gradual buildup of sympathy through failed attempted solutions.