10 MAR 2019 by ideonexus

 How Computational Review of Chess Games Revealed Narrativ...

Paradoxically, when other top players wrote about games in magazines and newspaper columns they often made more mistakes in their commentary than the players had made at the board. Even when the players themselves published analyses of their own games they were often less accurate than when they were playing the game. Strong moves were called errors, weak moves were praised. It was not only a few cases of journalists who were lousy players failing to comprehend the genius of the champions, or...
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02 MAR 2019 by ideonexus

 Chess is the Drosophila of Reasoning

Much as the Drosophila melanogaster fruit fly became a model organism for geneticists, chess became a Drosophila of reasoning. In the late 19th century, Alfred Binet hoped that understanding why certain people excelled at chess would unlock secrets of human thought. Sixty years later, Alan Turing wondered if a chess-playing machine might illuminate, in the words of Norbert Wiener, “whether this sort of ability represents an essential difference between the potentialities of the machine and ...
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02 MAR 2019 by ideonexus

 New Kind of Memory for AI

AI researchers have typically tried to get around the issues posed by by Montezuma’s Revenge and Pitfall! by instructing reinforcement-learning algorithms to explore randomly at times, while adding rewards for exploration—what’s known as “intrinsic motivation.” But the Uber researchers believe this fails to capture an important aspect of human curiosity. “We hypothesize that a major weakness of current intrinsic motivation algorithms is detachment,” they write. “Wherein the a...
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04 NOV 2018 by ideonexus

 A Computer Algorithm for Randomization

Back in the early days of computers, one of the more popular methods of generating a sequence of random numbers was to employ the following scheme: 1. Choose a starting number between 0 and 1. 2. Multiply the starting number by 4 ("stretch" it). Subtract 4 times the square of the starting number from the quantity obtained in step 2 ("fold" the interval back on itself in order to keep the final result in the same range). 3.Given a starting number between 0 and 1, we can use the proce-dure...
Folksonomies: algorithms randomization
Folksonomies: algorithms randomization
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From John Casti.

06 JAN 2018 by ideonexus

 The Personal Equation

Sounds like a "fuzzy set." Which comes into play when you try to categorize things that vary continuously into discrete groups. Can't be done without ambiguruity and bias. As a geneficist by the name of Pearl demonstrated when he had 15 scienfists sort the same 532 com kernels into yellow-starchy, yellow-sweet, white-starchy or whitesweet groupings. Each scientist came up with a different count. Instead of objectivity. Pearl discovered "personal equation," the slight nuance in perception each...
Folksonomies: perception
Folksonomies: perception
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22 SEP 2017 by ideonexus

 Algorithms are Subjective/Creative Things

he algorithm may be the essence of computer science – but it’s not precisely a scientific concept. An algorithm is a system, like plumbing or a military chain of command. It takes knowhow, calculation and creativity to make a system work properly. But some systems, like some armies, are much more reliable than others. A system is a human artefact, not a mathematical truism. The origins of the algorithm are unmistakably human, but human fallibility isn’t a quality that we associate with ...
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22 SEP 2017 by ideonexus

 Outsourcing our Thinking to Algorithms and Those Who Engi...

...even as an algorithm mindlessly implements its procedures – and even as it learns to see new patterns in the data – it reflects the minds of its creators, the motives of its trainers. Amazon and Netflix use algorithms to make recommendations about books and films. (One-third of purchases on Amazon come from these recommendations.) These algorithms seek to understand our tastes, and the tastes of like-minded consumers of culture. Yet the algorithms make fundamentally different recommend...
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25 MAY 2015 by ideonexus

 Jay Rosen: Information Overload

Filters in a digital world work not by removing what is filtered out; they simply don't select for it. The unselected material is still there, ready to be let through by someone else's filter. Intelligent filters, which is what we need, come in three kinds: A smart person who takes in a lot and tells you what you need to know. The ancient term for this is "editor." The front page of the New York Times still works this way. An algorithm that sifts through the choices other smart people have...
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