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.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“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

“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.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“To be human is to be 'a' human, a specific person with a life history and idiosyncrasy and point of view; artificial intelligence suggest that the line between intelligent machines and people blurs most when a puree is made of that identity.”
― Brian Christian, The Most Human Human: What Talking with Computers Teaches Us About What It Means to Be Alive

“Our judgments betray our expectations, and our expectations betray our experience. What we project about the future reveals a lot—about the world we live in, and about our own past.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“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.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“If you want to be a good intuitive Bayesian—if you want to naturally make good predictions, without having to think about what kind of prediction rule is appropriate—you need to protect your priors. Counterintuitively, that might mean turning off the news.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“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.”
― Brian Christian, The Most Human Human: What Talking with Computers Teaches Us About What It Means to Be Alive

“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.”
― Brian Christian, The Most Human Human: What Talking with Computers Teaches Us About What It Means to Be Alive

“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.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“The more helpful our phones get, the harder it is to be ourselves. For everyone out there fighting to write idiosyncratic, high-entropy, unpredictable, unruly text, swimming upstream of spell-check and predictive auto-completion: Don't let them banalize you. Keep fighting.”
― Brian Christian, The Most Human Human: What Talking with Computers Teaches Us About What It Means to Be Alive

“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.”
― Brian Christian, The Most Human Human: What Talking with Computers Teaches Us About What It Means to Be Alive

“Sorting something that you will never search is a complete waste; searching something you never sorted is merely inefficient.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“To try and fail is at least to learn; to fail to try is to suffer the inestimable loss of what might have been.”
― Brian Christian, Algorithms To Live By: The Computer Science of Human Decisions

“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.”
― Brian Christian, The Most Human Human: What Talking with Computers Teaches Us About What It Means to Be Alive

“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.”
― Brian Christian, The Most Human Human: What Talking with Computers Teaches Us About What It Means to Be Alive

“No choice recurs. We may get similar choices again, but never that exact one. Hesitation—inaction—is just as irrevocable as action. What the motorist, locked on the one-way road, is to space, we are to the fourth dimension: we truly pass this way but once.”
― Brian Christian, Algorithms To Live By: The Computer Science of Human Decisions

“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.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“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.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“The road to hell is paved with intractable recursions, bad equilibria, and information cascades.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“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.”
― Brian Christian, The Most Human Human: What Artificial Intelligence Teaches Us About Being Alive

“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.  …”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“Existence without essence is very stressful.”
― Brian Christian, The Most Human Human: What Talking with Computers Teaches Us About What It Means to Be Alive

“We say “brain fart” when we should really say “cache miss.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“They don’t need a therapist; they need an algorithm.”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“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. We”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions

“But if we really want to start fathoming someone, we need to get them speaking in sentences we can't finish.”
― Brian Christian

“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:”
― Brian Christian, The Most Human Human: What Talking with Computers Teaches Us About What It Means to Be Alive

“As Carl Sagan put it, “Science is a way of thinking much more than it is a body of knowledge.”
― 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. As”
― Brian Christian, Algorithms to Live By: The Computer Science of Human Decisions