Brian Christian Quotes
“Seemingly innocuous language like 'Oh, I'm flexible' or 'What do you want to do tonight?' has a dark computational underbelly that should make you think twice. It has the veneer of kindness about it, but it does two deeply alarming things. First, it passes the cognitive buck: 'Here's a problem, you handle it.' Second, by not stating your preferences, it invites the others to simulate or imagine them. And as we have seen, the simulation of the minds of others is one of the biggest computational challenges a mind (or machine) can ever face.”
“I think the reason novels are regarded to have so much more 'information' than films is that they outsource the scenic design and cinematography to the reader... This, for me, is a powerful argument for the value and potency of literature specifically. Movies don't demand as much from the player. Most people know this; at the end of the day you can be too beat to read but not yet too beat to watch television or listen to music.”
“When I fight off a disease bent on my cellular destruction, when I marvelously distribute energy and collect waste with astonishing alacrity even in my most seemingly fatigued moments, when I slip on ice and gyrate crazily but do not fall, when I unconsciously counter-steer my way into a sharp bicycle turn, taking advantage of physics I do not understand using a technique I am not even aware of using, when I somehow catch the dropped oranges before I know I've dropped them, when my wounds heal in my ignorance, I realize how much bigger I am than I think I am. And how much more important, nine times out of ten, those lower-level processes are to my overall well-being than the higher-level ones that tend to be the ones getting me bent out of shape or making me feel disappointed or proud.”
“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.”
“If changing strategies doesn’t help, you can try to change the game. And if that’s not possible, you can at least exercise some control about which games you choose to play. The road to hell is paved with intractable recursions, bad equilibria, and information cascades. Seek out games where honesty is the dominant strategy. Then just be yourself.”
“Grandmaster games are said to begin with novelty, which is the first move of the game that exits the book. It could be the fifth, it could be the thirty-fifth. We think about a chess game as beginning with move one and ending with checkmate. But this is not the case. The games begins when it gets out of book, and it end when it goes into book..And this is why Game 6 [between Garry Kasparov and Deep Blue] didn't count...Tripping and falling into a well on your way to the field of battle is not the same thing as dying in it...Deep Blue is only itself out of book; prior to that it is nothing. Just the ghosts of the game itself.”
“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.”
“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 and 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 Q.E.D.' Quod erat demonstrandum, the mathematical sign-off that Darwin himself restated in English: 'It being proved necessary to Marry.”
“Many of my all-time favorite movies are almost entirely verbal. The entire plot of My Dinner with Andre is “Wallace Shawn and Andre Gregory eat dinner.” The entire plot of Before Sunrise is “Ethan Hawke and Julie Delpy walk around Vienna.” But the dialogue takes us everywhere, and as Roger Ebert notes, of My Dinner with Andre, these films may be paradoxically among the most visually stimulating in the history of the cinema:”
“It’s fairly intuitive that never exploring is no way to live. But it’s also worth mentioning that never exploiting can be every bit as bad. In the computer science definition, exploitation actually comes to characterize many of what we consider to be life’s best moments. A family gathering together on the holidays is exploitation. So is a bookworm settling into a reading chair with a hot cup of coffee and a beloved favorite, or a band playing their greatest hits to a crowd of adoring fans, or a couple that has stood the test of time dancing to “their song.”
“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. …”
- Born: in Wilmington, DE, The United States.
- Description: Brian Christian is the author of The Most Human Human, which was named a Wall Street Journal bestseller, a New York Times Editors’ Choice, and a New Yorker favorite book of the year. He is the author, with Tom Griffiths, of Algorithms to Live By, a #1 Audible bestseller, Amazon best science book of the year and MIT Technology Review best book of the year.
Christian’s writing has been translated into nineteen languages, and has appeared in The New Yorker, The Atlantic, Wired, The Wall Street Journal, The Guardian, The Paris Review, and in scientific journals such as Cognitive Science. Christian has been featured on The Daily Show with Jon Stewart, Radiolab, and The Charlie Rose Show, and has lectured at Google, Facebook, Microsoft, the Santa Fe Institute, and the London School of Economics. His work has won several awards, including fellowships at Yaddo and the MacDowell Colony, publication in Best American Science & Nature Writing, and an award from the Academy of American Poets.
Born in Wilmington, Delaware, Christian holds degrees in philosophy, computer science, and poetry from Brown University and the University of Washington. A Visiting Scholar at the University of California, Berkeley, the Director of Technology at McSweeney’s Publishing, and an active open-source contributor to projects such as Ruby on Rails, he lives in San Francisco.