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Beautiful, Children, and Destiny: Rain fire Idon't even know-who you are. But sire, our troops! You should have You couldn't live with your own failure. Where did that bring you? Back to me gone for the head. I am inevitable Just do it! "A SOul for a Soul" They called me a madman All that for a drop of blood.... Reality can be whatever want Reality is often disapperthting No resurrections this time You should choose your words more carefully The hardest choices require the strongest wills. You're not the only one cursed with knowledge Daughter? | Did you do it? m gonna enjoy it Very, very much I'm sorry, little one. I hope they remember you Then you're more of a fool than I took you for I used the stones to destroy the stones And what did it cost? Yes This day extracts a heavy toll. I like him. Your planet was orn the hnkolll I'm the one who sttopped that Everything Perfectly balance... Even so, I'd hoped you'd sit in it one day. Gone, reduced to atoms This... does put a smile on my face. ...As all things should be And that.. is destiny fulfilled what wilh you do? Find them my children, and bring them to me I will shred this universe down to its last atom Dread it, run from it, destiny still arrives. wait. Let me guess, your home? Impossible Todav Ilost more than vou could ever know It was. The universe is finite. Its resources, finite. beautiful ...and it was A small price to nay for salvation. VAT Every Thanos meme from Infinity War; Endgame
Beautiful, Children, and Destiny: Rain fire
 Idon't even know-who you are.
 But sire, our troops!
 You should have
 You couldn't live with your own failure.
 Where did that bring you? Back to me
 gone for the head.
 I am inevitable
 Just do it!
 "A SOul for a Soul"
 They called me a madman
 All that for a drop of blood....
 Reality can be whatever want
 Reality is often disapperthting
 No resurrections this time
 You should choose your words more carefully
 The hardest choices require the
 strongest wills.
 You're not the only one cursed with knowledge
 Daughter?
 | Did you do it?
 m gonna enjoy it
 Very, very much
 I'm sorry, little one.
 I hope they remember you
 Then you're more of a fool than I took you for
 I used the stones to destroy the stones
 And what did it cost?
 Yes
 This day extracts a heavy toll.
 I like him.
 Your planet was orn the hnkolll
 I'm the one who sttopped that
 Everything
 Perfectly balance...
 Even so, I'd hoped you'd
 sit in it one day.
 Gone, reduced to atoms
 This... does put a smile on my face.
 ...As all things should be
 And that.. is destiny fulfilled
 what wilh you do?
 Find them my children, and bring them to me
 I will shred this universe down to its last atom
 Dread it, run from it, destiny still arrives.
 wait.
 Let me guess, your home?
 Impossible
 Todav Ilost more than vou could ever know
 It was.
 The universe is finite. Its resources, finite.
 beautiful
 ...and it was
 A small price to nay for salvation.
 VAT
Every Thanos meme from Infinity War; Endgame

Every Thanos meme from Infinity War; Endgame

Animals, Cars, and Google: Exploiting Similarities among Languages for Machine Translation Ilya Sutskever Quoc V. Le Tomas Mikolov Google Inc. Mountain Vie Google Inc. ountain View Google Inc. Mountain View 1lyasu@google.com qv1@google.com tmikolov@google.com infer missing Our study found that it is possible dictionary entries using distributed representations of words and phrases. We achieve this by learning a lincar projection between vector spaces that rep resent cach languag simple steps. ! languag l bilingual dictionary to leart Abstract Dictionaries and phrase tables are the basis machine tratslation sys . The method consists e dels of we build monolingual of modern sta puper develops a method ems omate the process of pe cnding diction word and method can trans catries ree monolingsal data and and ex- brase ubles. Our ext, we ase g large amounts of text. lincar projec- a smal hydraulic ning language siructures hd tion between the languages. At the ansdate any word that has bo time, we can ping be- seen in the mono- its vector representa- on larp languages from small bilingual data Hodistributed repres and learns a incas lingual corpora by pro from the source language space t language space. Once w target language sp word vector as the translation The rep using Bag-of words CBOW) models r of words eping between vector simpli the obtain the vector ram the ages Despite spaces issprisingly etior trala chieve almost 90% pr h and Spanish we output the most similar atations of languages are learned Continuous tien of words berwe This method he wed Csi rle assumption about the disributed Skip-gram or the days of "hydraulic ram" being translated ently proposed et al, 2013a) These language pai ecture th as "water sheep" are pretty much in the 1 Introduction Statistical mac ccme very sC developed for ycars. These systems rely on dictionaries anu h efforts to generate Figure 1 CS S why and how our method works e a P mbers and animals in En- Exploiting Similarities among Languages for Machine Translation lya Sutskever Google Inc. Mountain View lyasu@google.com Quoc V. Le Google Inc. Mountain View qv1@google.com Tomas Mikolov Google Inc. Mountain View tmikolov@google.com Our study found that it is possible to infer minsing entations Abstract nries using distributed re dictionary learning and phrases We spaces that reP lincar projection bet ncthod consists of two Dictionaries and p e the basis ranslation sys moders stals ccs a method thal resent cach language. simple steps. Fint languages using large amounts of test. Next. small tween the languages. At the build monolingual models of e use d es- e the process dig dictionaries and p geaera ahies Our word and phrae nethod can anguage strctures cotn lingal dala igal data. projec- gual dictionary to learn a l time, we can water seen in the mono ndate any word that has beo lingual corpora by projectin tion from the so language sp E obtain the vector arget langua word ween languages from we dstri its vector representa age space to the target sheep crion of words lece mapping between ve city oflective we can space, we output the most similar r as the translation languages Despin method is surg s or ra achive a English and The representations of languages are learned using the distributed S ds recently peoposed Baof Words(CBOW bo the Coetinuous eages so c ne dictionary auage pai the days of "hydraulic ram" being translated am a hesmedcls cars as "water sheep" are pretty much in the I Introduction Satistical machine ranlabo developed for years and tme These systems rely uaful in dictionaries aIN fforts to generate on bi Figue v y and how our orks 16 orn for numbers and anima these Help, hydraulic ram is haunting me
Animals, Cars, and Google: Exploiting Similarities among Languages for Machine Translation
 Ilya Sutskever
 Quoc V. Le
 Tomas Mikolov
 Google Inc.
 Mountain Vie
 Google Inc.
 ountain View
 Google Inc.
 Mountain View
 1lyasu@google.com
 qv1@google.com
 tmikolov@google.com
 infer missing
 Our study found that it is possible
 dictionary entries using distributed representations
 of words and phrases. We achieve this by learning
 a lincar projection between vector spaces that rep
 resent cach languag
 simple steps. !
 languag
 l bilingual dictionary to leart
 Abstract
 Dictionaries and phrase tables are the basis
 machine tratslation sys
 . The method consists e
 dels of
 we build monolingual
 of modern sta
 puper develops a method
 ems
 omate the process of pe
 cnding diction word and
 method can trans
 catries
 ree monolingsal data and
 and ex-
 brase ubles. Our
 ext, we ase
 g large amounts of text.
 lincar projec-
 a smal
 hydraulic
 ning language siructures hd
 tion between the languages. At the
 ansdate any word that has bo
 time, we can
 ping be-
 seen in the mono-
 its vector representa-
 on larp
 languages from small bilingual data
 Hodistributed repres
 and learns a incas
 lingual corpora by pro
 from the source language space t
 language space. Once w
 target language sp
 word vector as the translation
 The rep
 using
 Bag-of words CBOW) models r
 of words
 eping between vector
 simpli
 the
 obtain the vector
 ram
 the
 ages Despite
 spaces
 issprisingly etior trala
 chieve almost 90% pr h and Spanish
 we output the most similar
 atations of languages are learned
 Continuous
 tien of words berwe
 This method he wed Csi
 rle assumption about the
 disributed Skip-gram or
 the days of "hydraulic ram" being translated
 ently proposed
 et al, 2013a) These
 language pai
 ecture th
 as "water sheep" are pretty much in the
 1 Introduction
 Statistical mac ccme very sC
 developed for ycars.
 These systems rely on dictionaries anu
 h efforts to generate
 Figure 1 CS S
 why and how our method works
 e
 a P
 mbers and animals in En-
 Exploiting Similarities among Languages for Machine Translation
 lya Sutskever
 Google Inc.
 Mountain View
 lyasu@google.com
 Quoc V. Le
 Google Inc.
 Mountain View
 qv1@google.com
 Tomas Mikolov
 Google Inc.
 Mountain View
 tmikolov@google.com
 Our study found that it is possible to infer minsing
 entations
 Abstract
 nries using distributed re
 dictionary
 learning
 and phrases We spaces that reP
 lincar projection bet ncthod consists of two
 Dictionaries and p e the basis
 ranslation sys
 moders stals ccs a method thal
 resent cach language.
 simple steps. Fint
 languages using large amounts of test. Next.
 small
 tween the languages. At the
 build monolingual models of
 e use
 d es-
 e the process
 dig dictionaries and p
 geaera
 ahies Our
 word and phrae
 nethod can anguage strctures
 cotn lingal dala igal data.
 projec-
 gual dictionary to learn a l
 time, we can
 water
 seen in the mono
 ndate any word that has beo
 lingual corpora by projectin
 tion from the so
 language sp E obtain the vector
 arget langua
 word
 ween languages from
 we dstri
 its vector representa
 age space to the target
 sheep
 crion of words
 lece mapping between ve
 city
 oflective we can
 space, we output the most similar
 r as the translation
 languages Despin
 method is surg s or ra
 achive a English and
 The representations of languages are learned
 using the distributed S ds recently peoposed
 Baof Words(CBOW
 bo the
 Coetinuous
 eages so c
 ne dictionary
 auage pai
 the days of "hydraulic ram" being translated
 am
 a hesmedcls cars
 as "water sheep" are pretty much in the
 I Introduction
 Satistical machine ranlabo
 developed for years and tme
 These systems rely
 uaful in
 dictionaries aIN
 fforts to generate
 on bi
 Figue v
 y and how our
 orks 16
 orn for numbers and anima
 these
Help, hydraulic ram is haunting me

Help, hydraulic ram is haunting me