Монголчуудыг тоосондоо дарагдаж байх хойгуур...

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Тун удахгүй

http://i.imgur.com/36QVeo2.jpg

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AI байлдааны онгоц удирдаад эхлэв

New artificial intelligence
beats tactical experts in combat simulation

http://magazine.uc.edu/editors_picks/re ... alpha.html

[Walder Frey]

Муусайн шуналистууд сүрхий байна шүү

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Shunalismiig technology ustgana

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Muu dursamjiig ustgadag bolohod oir bna

Memory is being manipulated

https://www.quantamagazine.org/20160623 ... ipulation/

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Ruandad agaariin teever daraagiin shatand orov

http://mobile.nytimes.com/2016/04/05/te ... zolUX8DtLN

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Biologiig programchilj ehlev

http://m.phys.org/news/2016-03-language-cells.html

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Mongolchuudiig uursdiiguu Hytad,Solongois,Yaponuudaas havigui deeguur humuus gej yarihaar sanaa zovoh yum

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Ухаалаг Монголчууд бол ямар ч нийгэмд, ямар ч газар хөлөө олно. Харин Монгол улс маань улсаараа яах бол.

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Hyatduud single passenger drone turshij ehellee

http://en.people.cn/n3/2016/0612/c90000-9070731.html

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Нөгөө будааны хүүхэдүүд ийм юм хийж байна

[Саруул]

Энэ шуналистуудыг рекламддаг сэдэв юм уу? нкаад болжээн болжээн, цаагуураа муу капиталист гэж чамайг би мэдээд байсыма

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Яана даа тав алхахгүй өөрийгөө боож алах энэ мэдрэл баасыг

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Хиймэл Ухаан Про Покерчдын нусыг хацарт нь наав

Previous attempts to develop poker-playing AI that can exploit the mistakes of opponents—whether AI or human—have generally not been overly successful, says Tuomas Sandholm, a computer scientist at Carnegie Mellon University. Libratus instead focuses on improving its own play, which Sandholm describes as safer and more reliable compared to the riskier approach of trying to exploit opponent mistakes.

“We looked at fixing holes in our own strategy because it makes our own play safer and safer,” Sandholm says. “When you exploit opponents, you open yourself up to exploitation more and more.”

Even more important, the victory demonstrates how AI has likely surpassed the best humans at doing strategic reasoning in “imperfect information” games such as poker. The no-limit Texas Hold’em version of poker is a good example of an imperfect information game because players must deal with the uncertainty of two hidden cards and unrestricted bet sizes. An AI that performs well at no-limit Texas Hold’em could also potentially tackle real-world problems with similar levels of uncertainty.

“The algorithms we used are not poker specific,” Sandholm explains. “They take as input the rules of the game and output strategy.”

http://spectrum.ieee.org/automaton/robo ... er-players

Like AlphaGo before it, this system doesn’t try to solve the entire game, because there are simply too many possibilities to consider. Instead, both systems use deep learning to hone what they describe as an intuitive approach. According to its creators “[DeepStack] does not compute and store a complete strategy prior to play....It avoids reasoning about the entire remainder of the game by substituting the computation beyond a certain depth with a fast approximate estimate. This estimate can be thought of as DeepStack’s intuition: a gut feeling of the value of holding any possible private cards in any possible poker situation.”

http://www.theverge.com/2017/1/10/14220 ... as-hold-em

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[zaluunas]

NCA_D мариуханаа татаж орчлонгоос тасарч байх хойгуур...