Search Results for: science

Avi Rubin, a 44-year-old computer science professor at Johns Hopkins, is obsessed with the math behind Texas Hold ‘em:

When he began studying poker, Rubin frequently thought in terms of how a computer might model the game. Several disciplines were applicable—game theory, expert systems, machine learning, combinatorics. The latter is a branch of mathematics concerned with finite countable structures. The various combinations of cards in a poker hand are finite countable structures. As he trained himself to be a better player, Rubin would make up combinatorics poker problems, then solve them on a computer. He has considered studying the game by creating decision trees, branching diagrams that plot a chain of if-then options and are routine for a computer scientist. For example, he could start with a single hand, then chart all the variables—his position in a round of betting, the texture of the flop (that is, does it have potential to create strong hands like straights or flushes), whether he is playing against three others or heads-up against a single remaining opponent—to see what might happen. ‘For any given spot in the decision tree,’ he says, ‘I could come up with a probability distribution of different plays. Then I could write a learning program that I could use as a simulator on the computer and play a thousand times with particular settings, then tweak the settings and run it again to see if I do better, and work backward from it to infer why that was a better play in that situation. The thing is, there are so many variables and so many factors you rarely find yourself in a precise situation that you’ve studied. What you have to do is abstract out the reasoning used to get to that decision, then apply that logic and process to whatever situation you’re in.’

“Computing Texas Hold ‘em.” — Dale Keiger, Johns Hopkins Magazine

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Computing Texas Hold ’em

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Avi Rubin, a 44-year-old computer science professor at Johns Hopkins, is obsessed with the math behind Texas Hold ’em:

“When he began studying poker, Rubin frequently thought in terms of how a computer might model the game. Several disciplines were applicable—game theory, expert systems, machine learning, combinatorics. The latter is a branch of mathematics concerned with finite countable structures. The various combinations of cards in a poker hand are finite countable structures. As he trained himself to be a better player, Rubin would make up combinatorics poker problems, then solve them on a computer. He has considered studying the game by creating decision trees, branching diagrams that plot a chain of if-then options and are routine for a computer scientist. For example, he could start with a single hand, then chart all the variables—his position in a round of betting, the texture of the flop (that is, does it have potential to create strong hands like straights or flushes), whether he is playing against three others or heads-up against a single remaining opponent—to see what might happen. ‘For any given spot in the decision tree,’ he says, ‘I could come up with a probability distribution of different plays. Then I could write a learning program that I could use as a simulator on the computer and play a thousand times with particular settings, then tweak the settings and run it again to see if I do better, and work backward from it to infer why that was a better play in that situation. The thing is, there are so many variables and so many factors you rarely find yourself in a precise situation that you’ve studied. What you have to do is abstract out the reasoning used to get to that decision, then apply that logic and process to whatever situation you’re in.'”

Published: Jun 15, 2012
Length: 14 minutes (3,533 words)

Science fiction writer Ray Bradbury, a longtime Disney fan, finally goes to Disneyland.

The new appreciation of history begins with the responsibility in the hands of a man I trust, Walt Disney. In Disneyland he has proven again that the first function of architecture is to make men over make them wish to go on living, feed them fresh oxygen, grow them tall, delight their eyes, make them kind.

“The Machine-Tooled Happyland.” — Ray Bradbury, Holiday Mag, Oct. 1965

The Machine-Tooled Happyland

Longreads Pick

Science fiction writer Ray Bradbury, a longtime Disney fan, finally goes to Disneyland.

“The new appreciation of history begins with the responsibility in the hands of a man I trust, Walt Disney. In Disneyland he has proven again that the first function of architecture is to make men over make them wish to go on living, feed them fresh oxygen, grow them tall, delight their eyes, make them kind.”

Published: Oct 1, 1965
Length: 10 minutes (2,594 words)

A father recounts his family’s quest to diagnose a rare disease in their son:

We discovered that my son inherited two different (thus-far-unique) mutations in the same gene—the NGLY1 gene—which encodes the enzyme N-glycanase 1. Consequently, he cannot make this enzyme.

My son is the only human being known to lack this enzyme. Below, I’m documenting our journey to the unlikeliest of diagnoses. This is a story about the kind of hope that only science can provide. (An open access article in The Journal of Medical Genetics contains the detailed results from ground-breaking experiment that diagnosed him.)

“Hunting Down My Son’s Killer.” — Matt Might, Gizmodo

Hunting Down My Son’s Killer

Longreads Pick

A father recounts his family’s quest to diagnose a rare disease in their son:

“We discovered that my son inherited two different (thus-far-unique) mutations in the same gene—the NGLY1 gene—which encodes the enzyme N-glycanase 1. Consequently, he cannot make this enzyme.

“My son is the only human being known to lack this enzyme. Below, I’m documenting our journey to the unlikeliest of diagnoses. This is a story about the kind of hope that only science can provide. (An open access article in The Journal of Medical Genetics contains the detailed results from ground-breaking experiment that diagnosed him.)”

Author: Matt Might
Source: Gizmodo
Published: May 31, 2012
Length: 20 minutes (5,140 words)

A look back at James Watson’s book The Double Helix and the controversy it stirred in the science community.

In telling the story, he produced a great work of literary nonfiction. Watson expanded the boundaries of science writing to include not only the formal, public face of Nobel-winning discoveries but also the day-to-day life of working scientists—both inside and outside the lab.The Double Helixrejuvenated a genre that had been largely academic or hagiographic. Its success showed that there was and is an appetite for thestoryof science; that the stories can be human and exciting; that scientists can be flawed characters; that the whole endeavor doesn’t collapse if you depict it with something less than reverence.

Although the book caused an international scandal that winter, I don’t think any word of the controversy reached me at Classical High School. As a freshman, I read The Double Helix as a story of pure triumph. Now, of course, I can see what I couldn’t then: an epic of the loss of innocence, writ small and large. And I can see the arc of Watson’s life since 1968, which has been another epic of triumph and hubris, ending with a fall. So now I see the darkness around the shining cup.

“Laboratory Confidential.” — Jonathan Weiner, Columbia Journalism Review

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Laboratory Confidential

Longreads Pick

A look back at James Watson’s book The Double Helix and the controversy it stirred in the science community.

Watson expanded the boundaries of science writing to include not only the formal, public face of Nobel-winning discoveries but also the day-to-day life of working scientists—both inside and outside the lab. The Double Helix rejuvenated a genre that had been largely academic or hagiographic. Its success showed that there was and is an appetite for the story of science; that the stories can be human and exciting; that scientists can be flawed characters; that the whole endeavor doesn’t collapse if you depict it with something less than reverence.

Although the book caused an international scandal that winter, I don’t think any word of the controversy reached me at Classical High School. As a freshman, I read The Double Helix as a story of pure triumph. Now, of course, I can see what I couldn’t then: an epic of the loss of innocence, writ small and large. And I can see the arc of Watson’s life since 1968, which has been another epic of triumph and hubris, ending with a fall. So now I see the darkness around the shining cup.

Published: May 10, 2012
Length: 13 minutes (3,471 words)

Featured: Tech/Science site BBC Future’s #longreads page. Story picks about Olympic doping, the Titanic’s anniversary, plus more.

A Good Man Is Hard to Find (1953)

[Fiction] A grandmother’s ruminations on a Southern road trip:

The grandmother didn’t want to go to Florida. She wanted to visit some of her connections in east Tennessee and she was seizing at every chance to change Bailey’s mind. Bailey was the son she lived with, her only boy. He was sitting on the edge of his chair at the table, bent over the orange sports section of the Journal. ‘Now look here, Bailey,’ she said, ‘see here, read this,’ and she stood with one hand on her thin hip and the other rattling the newspaper at his bald head. ‘Here this fellow that calls himself The Misfit is aloose from the Federal Pen and headed toward Florida and you read here what it says he did to these people. Just you read it. I wouldn’t take my children in any direction with a criminal like that aloose in it. I couldn’t answer to my conscience if I did.’

“A Good Man Is Hard to Find.” (1953) Flannery O’Connor

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