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Guru Granth Sahib
Composition, Arrangement & Layout
ਜਪੁ | Jup
ਸੋ ਦਰੁ | So Dar
ਸੋਹਿਲਾ | Sohilaa
ਰਾਗੁ ਸਿਰੀਰਾਗੁ | Raag Siree-Raag
Gurbani (14-53)
Ashtpadiyan (53-71)
Gurbani (71-74)
Pahre (74-78)
Chhant (78-81)
Vanjara (81-82)
Vaar Siri Raag (83-91)
Bhagat Bani (91-93)
ਰਾਗੁ ਮਾਝ | Raag Maajh
Gurbani (94-109)
Ashtpadi (109)
Ashtpadiyan (110-129)
Ashtpadi (129-130)
Ashtpadiyan (130-133)
Bara Maha (133-136)
Din Raen (136-137)
Vaar Maajh Ki (137-150)
ਰਾਗੁ ਗਉੜੀ | Raag Gauree
Gurbani (151-185)
Quartets/Couplets (185-220)
Ashtpadiyan (220-234)
Karhalei (234-235)
Ashtpadiyan (235-242)
Chhant (242-249)
Baavan Akhari (250-262)
Sukhmani (262-296)
Thittee (296-300)
Gauree kii Vaar (300-323)
Gurbani (323-330)
Ashtpadiyan (330-340)
Baavan Akhari (340-343)
Thintteen (343-344)
Vaar Kabir (344-345)
Bhagat Bani (345-346)
ਰਾਗੁ ਆਸਾ | Raag Aasaa
Gurbani (347-348)
Chaupaday (348-364)
Panchpadde (364-365)
Kaafee (365-409)
Aasaavaree (409-411)
Ashtpadiyan (411-432)
Patee (432-435)
Chhant (435-462)
Vaar Aasaa (462-475)
Bhagat Bani (475-488)
ਰਾਗੁ ਗੂਜਰੀ | Raag Goojaree
Gurbani (489-503)
Ashtpadiyan (503-508)
Vaar Gujari (508-517)
Vaar Gujari (517-526)
ਰਾਗੁ ਦੇਵਗੰਧਾਰੀ | Raag Dayv-Gandhaaree
Gurbani (527-536)
ਰਾਗੁ ਬਿਹਾਗੜਾ | Raag Bihaagraa
Gurbani (537-556)
Chhant (538-548)
Vaar Bihaagraa (548-556)
ਰਾਗੁ ਵਡਹੰਸ | Raag Wadhans
Gurbani (557-564)
Ashtpadiyan (564-565)
Chhant (565-575)
Ghoriaan (575-578)
Alaahaniiaa (578-582)
Vaar Wadhans (582-594)
ਰਾਗੁ ਸੋਰਠਿ | Raag Sorath
Gurbani (595-634)
Asatpadhiya (634-642)
Vaar Sorath (642-659)
ਰਾਗੁ ਧਨਾਸਰੀ | Raag Dhanasaree
Gurbani (660-685)
Astpadhiya (685-687)
Chhant (687-691)
Bhagat Bani (691-695)
ਰਾਗੁ ਜੈਤਸਰੀ | Raag Jaitsree
Gurbani (696-703)
Chhant (703-705)
Vaar Jaitsaree (705-710)
Bhagat Bani (710)
ਰਾਗੁ ਟੋਡੀ | Raag Todee
ਰਾਗੁ ਬੈਰਾੜੀ | Raag Bairaaree
ਰਾਗੁ ਤਿਲੰਗ | Raag Tilang
Gurbani (721-727)
Bhagat Bani (727)
ਰਾਗੁ ਸੂਹੀ | Raag Suhi
Gurbani (728-750)
Ashtpadiyan (750-761)
Kaafee (761-762)
Suchajee (762)
Gunvantee (763)
Chhant (763-785)
Vaar Soohee (785-792)
Bhagat Bani (792-794)
ਰਾਗੁ ਬਿਲਾਵਲੁ | Raag Bilaaval
Gurbani (795-831)
Ashtpadiyan (831-838)
Thitteen (838-840)
Vaar Sat (841-843)
Chhant (843-848)
Vaar Bilaaval (849-855)
Bhagat Bani (855-858)
ਰਾਗੁ ਗੋਂਡ | Raag Gond
Gurbani (859-869)
Ashtpadiyan (869)
Bhagat Bani (870-875)
ਰਾਗੁ ਰਾਮਕਲੀ | Raag Ramkalee
Ashtpadiyan (902-916)
Gurbani (876-902)
Anand (917-922)
Sadd (923-924)
Chhant (924-929)
Dakhnee (929-938)
Sidh Gosat (938-946)
Vaar Ramkalee (947-968)
ਰਾਗੁ ਨਟ ਨਾਰਾਇਨ | Raag Nat Narayan
Gurbani (975-980)
Ashtpadiyan (980-983)
ਰਾਗੁ ਮਾਲੀ ਗਉੜਾ | Raag Maalee Gauraa
Gurbani (984-988)
Bhagat Bani (988)
ਰਾਗੁ ਮਾਰੂ | Raag Maaroo
Gurbani (889-1008)
Ashtpadiyan (1008-1014)
Kaafee (1014-1016)
Ashtpadiyan (1016-1019)
Anjulian (1019-1020)
Solhe (1020-1033)
Dakhni (1033-1043)
ਰਾਗੁ ਤੁਖਾਰੀ | Raag Tukhaari
Bara Maha (1107-1110)
Chhant (1110-1117)
ਰਾਗੁ ਕੇਦਾਰਾ | Raag Kedara
Gurbani (1118-1123)
Bhagat Bani (1123-1124)
ਰਾਗੁ ਭੈਰਉ | Raag Bhairo
Gurbani (1125-1152)
Partaal (1153)
Ashtpadiyan (1153-1167)
ਰਾਗੁ ਬਸੰਤੁ | Raag Basant
Gurbani (1168-1187)
Ashtpadiyan (1187-1193)
Vaar Basant (1193-1196)
ਰਾਗੁ ਸਾਰਗ | Raag Saarag
Gurbani (1197-1200)
Partaal (1200-1231)
Ashtpadiyan (1232-1236)
Chhant (1236-1237)
Vaar Saarang (1237-1253)
ਰਾਗੁ ਮਲਾਰ | Raag Malaar
Gurbani (1254-1293)
Partaal (1265-1273)
Ashtpadiyan (1273-1278)
Chhant (1278)
Vaar Malaar (1278-91)
Bhagat Bani (1292-93)
ਰਾਗੁ ਕਾਨੜਾ | Raag Kaanraa
Gurbani (1294-96)
Partaal (1296-1318)
Ashtpadiyan (1308-1312)
Chhant (1312)
Vaar Kaanraa
Bhagat Bani (1318)
ਰਾਗੁ ਕਲਿਆਨ | Raag Kalyaan
Gurbani (1319-23)
Ashtpadiyan (1323-26)
ਰਾਗੁ ਪ੍ਰਭਾਤੀ | Raag Prabhaatee
Gurbani (1327-1341)
Ashtpadiyan (1342-51)
ਰਾਗੁ ਜੈਜਾਵੰਤੀ | Raag Jaijaiwanti
Gurbani (1352-53)
Salok | Gatha | Phunahe | Chaubole | Swayiye
Sehskritee Mahala 1
Sehskritee Mahala 5
Gaathaa Mahala 5
Phunhay Mahala 5
Chaubolae Mahala 5
Shaloks Bhagat Kabir
Shaloks Sheikh Farid
Swaiyyae Mahala 5
Swaiyyae in Praise of Gurus
Shaloks in Addition To Vaars
Shalok Ninth Mehl
Mundavanee Mehl 5
ਰਾਗ ਮਾਲਾ, Raag Maalaa
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Law Of Entropy Linked To Evolution Of Intelligence
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<blockquote data-quote="spnadmin" data-source="post: 183343" data-attributes="member: 35"><p><strong></strong></p><p><strong>Entropy law linked to intelligence, say researchers</strong></p><p>By Jason Palmer</p><p></p><p><a href="http://www.bbc.co.uk/news/science-environment-22261742" target="_blank">http://www.bbc.co.uk/news/science-environment-22261742</a></p><p></p><p>A modification to one of the most fundamental laws of physics may provide a link to the rise of intelligence, cooperation - even upright walking.</p><p></p><p>The idea of entropy describes the way in which the Universe heads inexorably toward a higher state of disorder.</p><p></p><p>A mathematical model in Physical Review Letters proposes that systems maximise entropy in the present and the future.</p><p></p><p>Simple simulations based on the idea reproduce a variety of real-world cases that reflect intelligent behaviour.</p><p></p><p>The idea of entropy is fundamentally an intuitive one - that the Universe tends in general to a more disordered state.</p><p></p><p>The classic example is a dropped cup: it will smash into pieces, but those pieces will never spontaneously recombine back into a cup. Analogously, a hot cup of coffee will always cool down if left - it will never draw warmth from a room to heat back up.</p><p></p><p>But the idea of "causal entropy" goes further, suggesting that a given physical system not only maximises the entropy within its current conditions, but that it reaches a state that will allow it more entropy - in a real sense, more options - in the future.</p><p></p><p>Alex Wissner-Gross of Harvard University and the Massachusetts Institute of Technology in the US and Cameron Freer from the University of Hawaii at Manoa, have now put together a mathematical model that ties this causal entropy idea - evident in a range of recent studies - into a single framework.</p><p></p><p>"In the past 10 to 15 years, there have been many hints from a variety of different disciplines that there was a deep link between entropy production and intelligence," Dr Wissner-Gross told BBC News.</p><p></p><p></p><p>The laws of thermodynamics</p><p></p><p> First law: Energy can be neither created nor destroyed (although, thanks to Einstein's most famous equation E=mc2, energy can come from or be turned into mass)</p><p> Second law: The entropy of an isolated system always rises. But put some energy in and order can be achieved - in a crystal of salt, in humans, even in galaxies</p><p> Third Law: As the temperature of an ordered system - like that salt crystal - approaches "absolute zero", entropy approaches its lowest level. But never zero, because everything, everywhere is at least a little bit disordered</p><p></p><p>"This paper is really the first result that clarifies what that link precisely is... to the point that it's prescriptive - it actually allows you to calculate in a sensible way answers to questions that couldn't reasonably be answered before."</p><p></p><p>The simplistic model considers a number of examples, such as a pendulum hanging from a moving cart. Simulations of the causal entropy idea show that the pendulum ends up pointing upward - an unstable situation, but one from which the pendulum can explore a wider variety of positions.</p><p></p><p>The researchers liken this to the development of upright walking.</p><p></p><p>Further simulations showed how the same idea could drive the development of tool use, social network formation and cooperation, and even the maximisation of profit in a simple financial market.</p><p></p><p>"While there were hints from a variety of other fields such as cosmology, it was so enormously surprising to see that one could take these principles, apply them to simple systems, and effectively for free have such behaviours pop out," Dr Wissner-Gross said.</p><p>'Beyond luck'</p><p></p><p>Raphael Bousso of the University of California Berkeley said: "It has always mystified me how well this principle models intelligent observers, and it would be wonderful if Alex's work could shed some light on this."</p><p>Simulation of ships trading Entropy-maximised simulations of trade routes saw ships spontaneously discover the Panama Canal</p><p></p><p>Prof Bousso showed in a 2007 paper in Physical Review D that models of the Universe that incorporated causal entropy were more likely to come up with a Universe that contains intelligent observers - that is, us.</p><p></p><p>However, he cautions that although the new paper bolsters the case for causal entropy, the idea still lacks explanatory power.</p><p></p><p>"The paper argues that intelligent behaviour, which is hard to quantify, can be reduced to maximising one's options, which is relatively easy to quantify. But it cannot explain intelligent behaviour from first principles," he told BBC News.</p><p></p><p>"It cannot explain how that 'intelligent agent' evolved in the first place, and why it seeks to maximise future options."</p><p></p><p>Axel Kleidon of the Max Planck Institute for Biogeochemistry in Germany, who authored a 2010 paper in Physics of Life Reviews using maximised entropy to consider the machinery of life on Earth, said that the work "shows some very intriguing examples" but that only time would tell if causal entropy was as fundamental as it may seem.</p><p></p><p>"It seems that it is beyond just luck and coincidence," he told BBC News.</p><p></p><p>"On the other hand, I know from my own research that applying thermodynamics to real-world systems is anything but simple and straightforward... I think it is through more examples that (we will see) how practical their approach will be, compared to other thermodynamic approaches."</p></blockquote><p></p>
[QUOTE="spnadmin, post: 183343, member: 35"] [B] Entropy law linked to intelligence, say researchers[/B] By Jason Palmer [url]http://www.bbc.co.uk/news/science-environment-22261742[/url] A modification to one of the most fundamental laws of physics may provide a link to the rise of intelligence, cooperation - even upright walking. The idea of entropy describes the way in which the Universe heads inexorably toward a higher state of disorder. A mathematical model in Physical Review Letters proposes that systems maximise entropy in the present and the future. Simple simulations based on the idea reproduce a variety of real-world cases that reflect intelligent behaviour. The idea of entropy is fundamentally an intuitive one - that the Universe tends in general to a more disordered state. The classic example is a dropped cup: it will smash into pieces, but those pieces will never spontaneously recombine back into a cup. Analogously, a hot cup of coffee will always cool down if left - it will never draw warmth from a room to heat back up. But the idea of "causal entropy" goes further, suggesting that a given physical system not only maximises the entropy within its current conditions, but that it reaches a state that will allow it more entropy - in a real sense, more options - in the future. Alex Wissner-Gross of Harvard University and the Massachusetts Institute of Technology in the US and Cameron Freer from the University of Hawaii at Manoa, have now put together a mathematical model that ties this causal entropy idea - evident in a range of recent studies - into a single framework. "In the past 10 to 15 years, there have been many hints from a variety of different disciplines that there was a deep link between entropy production and intelligence," Dr Wissner-Gross told BBC News. The laws of thermodynamics First law: Energy can be neither created nor destroyed (although, thanks to Einstein's most famous equation E=mc2, energy can come from or be turned into mass) Second law: The entropy of an isolated system always rises. But put some energy in and order can be achieved - in a crystal of salt, in humans, even in galaxies Third Law: As the temperature of an ordered system - like that salt crystal - approaches "absolute zero", entropy approaches its lowest level. But never zero, because everything, everywhere is at least a little bit disordered "This paper is really the first result that clarifies what that link precisely is... to the point that it's prescriptive - it actually allows you to calculate in a sensible way answers to questions that couldn't reasonably be answered before." The simplistic model considers a number of examples, such as a pendulum hanging from a moving cart. Simulations of the causal entropy idea show that the pendulum ends up pointing upward - an unstable situation, but one from which the pendulum can explore a wider variety of positions. The researchers liken this to the development of upright walking. Further simulations showed how the same idea could drive the development of tool use, social network formation and cooperation, and even the maximisation of profit in a simple financial market. "While there were hints from a variety of other fields such as cosmology, it was so enormously surprising to see that one could take these principles, apply them to simple systems, and effectively for free have such behaviours pop out," Dr Wissner-Gross said. 'Beyond luck' Raphael Bousso of the University of California Berkeley said: "It has always mystified me how well this principle models intelligent observers, and it would be wonderful if Alex's work could shed some light on this." Simulation of ships trading Entropy-maximised simulations of trade routes saw ships spontaneously discover the Panama Canal Prof Bousso showed in a 2007 paper in Physical Review D that models of the Universe that incorporated causal entropy were more likely to come up with a Universe that contains intelligent observers - that is, us. However, he cautions that although the new paper bolsters the case for causal entropy, the idea still lacks explanatory power. "The paper argues that intelligent behaviour, which is hard to quantify, can be reduced to maximising one's options, which is relatively easy to quantify. But it cannot explain intelligent behaviour from first principles," he told BBC News. "It cannot explain how that 'intelligent agent' evolved in the first place, and why it seeks to maximise future options." Axel Kleidon of the Max Planck Institute for Biogeochemistry in Germany, who authored a 2010 paper in Physics of Life Reviews using maximised entropy to consider the machinery of life on Earth, said that the work "shows some very intriguing examples" but that only time would tell if causal entropy was as fundamental as it may seem. "It seems that it is beyond just luck and coincidence," he told BBC News. "On the other hand, I know from my own research that applying thermodynamics to real-world systems is anything but simple and straightforward... I think it is through more examples that (we will see) how practical their approach will be, compared to other thermodynamic approaches." [/QUOTE]
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