Unlike Chess Quotes

In life, unlike chess, the game continues after checkmate.

The queen was my favorite chess piece. Unlike the women I knew in real life, she was powerful. Her job was to defend her husband at all costs, because while he was weak and practically defenseless—only allowed to move one square at a time—she was the strongest player on the board, hindered by no restrictions at all.

Most Debunkers spent their money on actual things, rather than just buying anything they could swallow, smoke or snort. Unlike Chess.

Outcomes don’t tell us what’s our fault and what isn’t, what we should take credit for and what we shouldn’t. Unlike in chess, we can’t simply work backward from the quality of the outcome to determine the quality of our beliefs or decisions. This makes learning from outcomes a pretty haphazard process.

A deep laugh stirred in his chest, and his thumb brushed over the backs of her fingers before he withdrew his hand. She felt the rasp of a callus on his thumb, the sensation not unlike the tingling scrape of a cat’s tongue. Bemused by her own response to him, Annabelle looked down at the chess piece in her hand. “That is the queen—the most powerful piece on the board. She can move in any direction, and go as far as she wishes.” There was nothing overtly suggestive in his manner of speaking …but when he spoke softly, as he was doing at that moment, there was a husky depth in his voice that made her toes curl inside her slippers. “More powerful than the king?” she asked. “Yes. The king can only move one square at a time. But the king is the most important piece.” “Why is he more important than the queen if he’s not the most powerful?” “Because once he is captured, the game is over.

I press my lips together. “He can be the current champion and still be having a bad month.” “Unlikely, since he won Sweden Chess last week.

In the economic sphere too, the ability to hold a hammer or press a button is becoming less valuable than before. In the past, there were many things only humans could do. But now robots and computers are catching up, and may soon outperform humans in most tasks. True, computers function very differently from humans, and it seems unlikely that computers will become humanlike any time soon. In particular, it doesn’t seem that computers are about to gain consciousness, and to start experiencing emotions and sensations. Over the last decades there has been an immense advance in computer intelligence, but there has been exactly zero advance in computer consciousness. As far as we know, computers in 2016 are no more conscious than their prototypes in the 1950s. However, we are on the brink of a momentous revolution. Humans are in danger of losing their value, because intelligence is decoupling from consciousness. Until today, high intelligence always went hand in hand with a developed consciousness. Only conscious beings could perform tasks that required a lot of intelligence, such as playing chess, driving cars, diagnosing diseases or identifying terrorists. However, we are now developing new types of non-conscious intelligence that can perform such tasks far better than humans. For all these tasks are based on pattern recognition, and non-conscious algorithms may soon excel human consciousness in recognising patterns. This raises a novel question: which of the two is really important, intelligence or consciousness? As long as they went hand in hand, debating their relative value was just a pastime for philosophers. But in the twenty-first century, this is becoming an urgent political and economic issue. And it is sobering to realise that, at least for armies and corporations, the answer is straightforward: intelligence is mandatory but consciousness is optional.

I do not know how old I was when I learned to play chess. I could not have been older than eight, because I still have a chessboard on whose side my father inscribed, with a soldering iron, “Saša Hemon 1972.” I loved the board more than chess—it was one of the first things I owned. Its materiality was enchanting to me: the smell of burnt wood that lingered long after my father had branded it; the rattle of the thickly varnished pieces inside, the smacking sound they made when I put them down, the board’s hollow wooden echo. I can even recall the taste—the queen’s tip was pleasantly suckable; the pawns’ round heads, not unlike nipples, were sweet. The board is still at our place in Sarajevo, and, even if I haven’t played a game on it in decades, it is still my most cherished possession, providing incontrovertible evidence that there once lived a boy who used to be me.

Freenet is unlike any other anonymizing beast on the entire internet. It takes quite a wizardly mind to crack its protection and to that, it is a bit like chess: easy to grasp the basics, long and difficult to become a master. Built in 2000, Freenet is a vast, encrypted datastore spanning thousands of connected computers across the globe, all distributing encrypted contents from one computer to another. To this end, it is somewhat similar to a P2P program like Emule. Except with eEule, every file, whether it mp3, rar or iso is out there in the open for

In 1997 an IBM computer called Deep Blue defeated the world chess champion Garry Kasparov, and unlike its predecessors, it did not just evaluate trillions of moves by brute force but was fitted with strategies that intelligently responded to patterns in the game. [Y]ou might still object that chess is an artificial world with discrete moves and a clear winner, perfectly suited to the rule-crunching of a computer. People, on the other hand, live in a messy world offering unlimited moves and nebulous goals. Surely this requires human creativity and intuition — which is why everyone knows that computers will never compose a symphony, write a story, or paint a picture. But everyone may be wrong. Recent artificial intelligence systems have written credible short stories, composed convincing Mozart-like symphonies, drawn appealing pictures of people and landscapes, and conceived clever ideas for advertisements. None of this is to say that the brain works like a digital computer, that artificial intelligence will ever duplicate the human mind, or that computers are conscious in the sense of having first-person subjective experience. But it does suggest that reasoning, intelligence, imagination, and creativity are forms of information processing, a well-understood physical process. Cognitive science, with the help of the computational theory of mind, has exorcised at least one ghost from the machine.

Similarly, the brains of mice that have learned many tasks are slightly different from the brains of other mice that have not learned these tasks. It is not so much that the number of neurons has changed, but rather that the nature of the neural connections has been altered by the learning process. In other words, learning actually changes the structure of the brain. This raises the old adage “practice makes perfect.” Canadian psychologist Dr. Donald Hebb discovered an important fact about the wiring of the brain: the more we exercise certain skills, the more certain pathways in our brains become reinforced, so the task becomes easier. Unlike a digital computer, which is just as dumb today as it was yesterday, the brain is a learning machine with the ability to rewire its neural pathways every time it learns something. This is a fundamental difference between a digital computer and the brain. This lesson applies not only to London taxicab drivers, but also to accomplished concert musicians as well. According to psychologist Dr. K. Anders Ericsson and colleagues, who studied master violinists at Berlin’s elite Academy of Music, top concert violinists could easily rack up ten thousand hours of grueling practice by the time they were twenty years old, practicing more than thirty hours per week. By contrast, he found that students who were merely exceptional studied only eight thousand hours or fewer, and future music teachers practiced only a total of four thousand hours. Neurologist Daniel Levitin says, “The emerging picture from such studies is that ten thousand hours of practice is required to achieve the level of mastery associated with being a world-class expert—in anything.… In study after study, of composers, basketball players, fiction writers, ice skaters, concert pianists, chess players, master criminals, and what have you, this number comes up again and again.” Malcolm Gladwell, writing in the book Outliers, calls this the “10,000-hour rule.

Natalie’s house, not least because of the seventeen-inch Zenith, inside a pale wood cabinet, the biggest television Miri had ever seen. Her grandmother had a set but it was small with rabbit ears and sometimes the picture was snowy. The furniture in the Osners’ den all matched, the beige sofas and club chairs arranged around a Danish modern coffee table, with its neat stacks of magazines—Life, Look, Scientific American, National Geographic. A cloth bag with a wood handle, holding Mrs. Osner’s latest needlepoint project, sat on one of the chairs. A complete set of the Encyclopaedia Britannica took up three shelves of the bookcase, along with family photos, including one of Natalie at summer camp, in jodhpurs, atop a sleek black horse, holding her ribbons, and another of her little sister, Fern, perched on a pony. In one corner of the room was a game table with a chess set standing ready, not that she and Natalie knew how to play, but Natalie’s older brother, Steve, did and sometimes he and Dr. Osner would play for hours.

Swimming is about the only physical activity during which I am unlikely to injure anyone. I am also good at chess, which I feel should count as physical activity because you have to reach over and move pieces all the time. Some of the boards are quite large, so one must lean AND reach at the same time, thereby expending even more energy.

these researchers figured they’d go straight to the source. This approach mimics the brain’s underlying architecture, constructing layers of artificial neurons that can receive and transmit information in a structure akin to our networks of biological neurons. Unlike the rule-based approach, builders of neural networks generally do not give the networks rules to follow in making decisions. They simply feed lots and lots of examples of a given phenomenon—pictures, chess games, sounds—into the neural networks and let the networks themselves identify patterns within the data. In other words, the less human interference, the better.

Outcomes don’t tell us what’s our fault and what isn’t, what we should take credit for and what we shouldn’t. Unlike in chess, we can’t simply work backward from the quality of the outcome to determine the quality of our beliefs or decisions. This makes learning from outcomes a pretty haphazard process. A negative outcome could be a signal to go in and examine our decision-making. That outcome could also be due to bad luck, unrelated to our decision, in which case treating that outcome as a signal to change future decisions would be a mistake. A good outcome could signal that we made a good decision. It could also mean that we got lucky, in which case we would be making a mistake to use that outcome as a signal to repeat that decision in the future.

By the time I began my Ph.D., the field of artificial intelligence had forked into two camps: the “rule-based” approach and the “neural networks” approach. Researchers in the rule-based camp (also sometimes called “symbolic systems” or “expert systems”) attempted to teach computers to think by encoding a series of logical rules: If X, then Y. This approach worked well for simple and well-defined games (“toy problems”) but fell apart when the universe of possible choices or moves expanded. To make the software more applicable to real-world problems, the rule-based camp tried interviewing experts in the problems being tackled and then coding their wisdom into the program’s decision-making (hence the “expert systems” moniker). The “neural networks” camp, however, took a different approach. Instead of trying to teach the computer the rules that had been mastered by a human brain, these practitioners tried to reconstruct the human brain itself. Given that the tangled webs of neurons in animal brains were the only thing capable of intelligence as we knew it, these researchers figured they’d go straight to the source. This approach mimics the brain’s underlying architecture, constructing layers of artificial neurons that can receive and transmit information in a structure akin to our networks of biological neurons. Unlike the rule-based approach, builders of neural networks generally do not give the networks rules to follow in making decisions. They simply feed lots and lots of examples of a given phenomenon—pictures, chess games, sounds—into the neural networks and let the networks themselves identify patterns within the data. In other words, the less human interference, the better.

At its most basic, the game is a strategy game similar to chess in that it stresses positional supremacy. Unlike chess which has a set of permissible moves, the game of Go has very few rules. In the game, adaptability and patience are the most valued traits, and as such, the contest is often much less about the game itself than about the contest of wills between players.

But of course, the most important trick in beating the S-chool game is to know that it is a game, as abstract, unreal, and useless as chess, and that beating it is a trick. The game is important only because (as with chess) there are rewards for playing it well, and (unlike chess) penalties for playing it badly. This is something that almost all successful students know, almost by instinct. I sensed it at ten, and knew it thoroughly and consciously by the time I was thirteen.

Unlike the rankings published for most sports, the chess rating system is extremely accurate; for practical purposes, your rating is a nearly perfect indicator of your ability.

For poker, unlike quite any other game, mirrors life. It isn’t the roulette wheel of pure chance, nor is it the chess of mathematical elegance and perfect information.

In life, unlike chess, the game continues after checkmate. —Isaac Asimov Writer

In life, unlike chess, the game continues after checkmate.

Except for a handful, chess players don’t have such illusions. The game has a severe analytic quality that makes self-deception difficult. Unlike the undiscovered poet who, despite the harsh criticism of his peers, lives on his fantasies for the day that he will be recognized as the next Dylan Thomas, even a young chess player can usually gauge his talent. When Josh was six, he played several games against a pudgy thirteen-year-old who was the top player on his high school team. He beat Josh every time, but a couple of the games were close, and afterwards the boy seemed gloomy about his performance. He explained that if he didn’t make significant improvement during the next year, he would wind up as just another wood-pusher. Despite his celebrity in school, he seemed to know that he didn’t have it. While

Walking up the tree-lined boulevard toward the center always brings out my inner Igor. I often run into Wincing Evan, so called because of the flinch—bordering on a Tourette’s-like seizure—he goes into whenever he spots Dev and me approaching. Head down, he’ll actually scamper across the street to avoid saying hello. In some ways, Evan is a figure of the type I aspire to cut. He translates (let’s say) Gogol. He publishes in The New York Review of Books and abroad. Unlike the blocky Boston bankers who abound in Harvard Square, he cruises in for Parents’ Day wearing a fluid flannel coat with French tailoring, for he and his professor wife (a comp-lit professor whose easy red-lipped smile could’ve sold lipstick) summer overseas often enough to use summer as a verb. Their immaculately turned-out son—Jonathan, age under four years—has shining hair and a good start on French and German. He’s a chess player with a princely manner. I swear if his voice were a little deeper, he could join the diplomatic corps. I once saw Dev, whose sandwich that day was, as most days, a peon’s peanut butter and jelly, try to urge Jonathan into swapping lunches. Young Jonathan peeled back one corner of his seven-grain bread carefully enough not to break the crust. Dev peered in. Jonathan said, Mine is brie and kiwi fruit.