Охота на электроовец. Большая книга искусственного интеллекта - читать бесплатно онлайн полную версию книги автора Сергей Сергеевич Марков (ч. notes)

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^* Официально: Первая научно-популярная библиотека «Научка» (ГБУК г.Москвы ОКЦ ЦАО ЦДБ 14 "Научка"). — Здесь и далее примечания автора.

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Russell S. J., Norvig P. (2016). Artificial Intelligence: A Modern Approach. Pearson // https://books.google.ru/books?id=XS9CjwEACAAJ

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^* Исполнитель роли агента Смита в фильме «Матрица» (1999).

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^* Двойной слепой метод — подход, когда ни задающий вопросы, ни взаимодействующие с ним организаторы сами не знают, кто из участников теста является машиной и есть ли вообще машина среди участников теста; то есть задача для жюри должна быть сформулирована следующим образом: «Выберите один из вариантов: только испытуемый 1 является машиной, только испытуемый 2 является машиной, оба испытуемых являются машинами, оба испытуемых являются людьми».

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Кокшотт У. П., Микаэльсон Г., Коттрел А. (2017). Бёттке, синтаксис и тест Тьюринга / Пер. с англ. Горлова А. В., Маркова С. С // https://22century.ru/popular-science-publications/boettke-syntax-and-the-turing-test

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^* Способность мозга находить причинно-следственные связи.

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^** Представление о том, что в основе разума лежат квантовомеханические эффекты, принципиально невоспроизводимые средствами классической механики.

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^*** Наборы визуальных тестов для оценки способности системы находить простые закономерности, предложенные советским учёным Михаилом Бонгардом.

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^**** CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart) — полностью автоматизированный публичный тест Тьюринга для различения компьютеров и людей.

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^* Пиксель (от англ. сокращения от pictures element) — наименьший элемент двумерного цифрового изображения.

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^* Хемоинформатика (химическая информатика, молекулярная информатика) — применение методов информатики при решении химических проблем.

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^* Куфическое письмо — один из наиболее древних видов арабского письма, созданный в конце VIII в.; сыграл значительную роль в дальнейшем развитии всей арабской каллиграфии.

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Freeth T., Higgon D., Dacanalis A., MacDonald L., Georgakopoulou M., Wojcik A. (2021). A Model of the Cosmos in the ancient Greek Antikythera Mechanism / Scientific Reports, Vol. 11, Article number: 5821 // https://doi.org/10.1038/s41598-021-84310-w

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Experts recreate a mechanical Cosmos for the world’s first computer (2021) / UCL News, 12 March 2021 // https://www.ucl.ac.uk/news/2021/mar/experts-recreate-mechanical-cosmos-worlds-first-computer

137

Byzantine portable universal altitude sundial with geared calendrical device // http://collection.sciencemuseum.org.uk/objects/co1082/byzantine-portable-universal-altitude-sundial-with-geared-calendrical-device-sundial-perpetual-calendar

138

Pappus, Hultsch F. O. (1878). Pappi Alexandrini collectionis quae supersunt. Apud Weidmannos // https://archive.org/stream/pappialexandrin02hultgoog#page/n33/mode/2up

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Freeth, Tony; Jones, Alexander (2012). The Cosmos in the Antikythera Mechanism. Institute for the Study of the Ancient World // http://dlib.nyu.edu/awdl/isaw/isaw-papers/4/

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^* Пер. Е. Красновой.

142

Математика XVII столетия (1970) // История математики в 3 т / под ред. А. П. Юшкевича. — М.: Наука. Т. II. С. 54–48 // http://ilib.mccme.ru/djvu/istoria/istmat2.htm

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Карпушкина Н. (2011). Решётчатое умножение // Наука и жизнь. №2 // https://www.nkj.ru/archive/articles/19204/

145

^* Османская миниатюра — форма искусства в Османской империи, разновидность живописи, изображающая сцены войн, охоты, значимых для двора и страны событий, уклад и образ жизни людей.

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^** «Девширме» («налог кровью») — система принудительного набора мальчиков из христианских семей для их последующего воспитания и дальнейшей службы в роли «капыкулу» (kapıkulları, «государевы рабы») — лиц рабского статуса на государственной и военной службе. Большая часть чиновников и военных Османской империи в XV–XVI вв. состояла именно из призванных по девширме лиц.

147

Stoianovich T. (2015). Balkan Worlds: The First and Last Europe: The First and Last Europe. Taylor & Francis // https://books.google.ru/books?id=lKVzCQAAQBAJ

148

Woodhead C. (2011). The Ottoman World. Taylor & Francis // https://books.google.ru/books?id=jt_FBQAAQBAJ

149

Corlu M. S., Burlbaw L. M., Capraro R. M., Han S., Çorlu M. A. (2010). The Ottoman palace school and the man with multiple talents, Matrakçı Nasuh / Journal of the Korea Society of Mathematical Education Series D: Research in Mathematical Education, Vol. 14, Iss. 1, pp. 19–31 // https://www.academia.edu/480968/Corlu_M._S._Burlbaw_L._M._Capraro_R._M._Han_S._and_%C3%87orlu_M._A._2010_._The_Ottoman_palace_school_and_the_man_with_multiple_talents_Matrak%C3%A7%C4%B1_Nasuh._Journal_of_the_Korea_Society_of_Mathematical_Education_Series_D_Research_in_Mathematical_Education_14_1_19_31

150

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Karagöz A. (2013). Nasûh Es-Silâhî'nin Umdetü'l Hisâb Adlı Eseri (89b-179a) (İnceleme-Metin-Dizin-Tıpkıbasım) // https://tezarsivi.com/nasuh-es-silahinin-umdetul-hisab-adli-eseri-89b-179a-inceleme-metin-dizin-tipkibasim

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Dalakov G. The Rechenuhr (Calculating Clock) of Wilhelm Schickard / History of Computers: hardware, software, internet… // http://history-computer.com/MechanicalCalculators/Pioneers/Schickard.html

155

^* Сенешаль — глава региональной системы правосудия во Франции в XVII в.

156

Adamson D. (1994). Blaise Pascal: Mathematician, Physicist and Thinker about God. Palgrave Macmillan UK // https://books.google.ru/books?id=qpmGDAAAQBAJ

157

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158

Тарасов Б. Н. (2006). Паскаль. — М.: Молодая гвардия // https://books.google.ru/books?id=pq59AAAAMAAJ

159

Dalakov G. The Pascaline of Blaise Pascal / History of Computers: hardware, software, internet… // http://history-computer.com/MechanicalCalculators/Pioneers/Pascal.html

160

http://www.historicalstatistics.org/Currencyconverter.html

161

https://www.in2013dollars.com/us/inflation/2015?amount=2310

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Dalakov G. The arithmometer of Thomas de Colmar / History of Computers: hardware, software, internet… // http://history-computer.com/MechanicalCalculators/19thCentury/Colmar.html

163

Leibniz Invents the Stepped Drum Gear Calculator / Jeremy Norman's HistoryOfInformation.com // http://www.historyofinformation.com/expanded.php?id=453

164

Doron Swade. Babbage, Charles / Oxford Dictionary of National Biography // https://doi.org/10.1093/ref:odnb/962

165

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166

LMA/4050. REVEREND STEPHEN FREEMAN'S SCHOOL PONDERS END / London Metropolitan Archives: City of London // http://discovery.nationalarchives.gov.uk/details/r/db8b56cf-ea51-4c0b-ad46-320a2391d714

167

Woodhouse R. (1803). The Principles of Analytical Calculation. Cambridge University Press // https://books.google.ru/books?id=rEbnAAAAMAAJ

168

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A Report of the Trial of the Reverend Thomas Jephson, for a Misdemeanor, at the Cambridge Summer Assizes, 1823, on Wednesday, July 23, Before Mr. Serjeant Bosanquet, and a Common Jury (1823) // https://books.google.ru/books?id=gZPfdOebxO0C

174

Simister L. (2015). Charles Babbage from the Beginning. Lulu.com // https://books.google.ru/books?id=YqJmCgAAQBAJ

175

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176

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177

Computing the Nautical Almanac, Called the "Seaman's Bible" / Jeremy Norman's HistoryOfInformation.com // http://www.historyofinformation.com/expanded.php?id=485

178

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182

Computing the Nautical Almanac, Called the "Seaman's Bible" / Jeremy Norman's HistoryOfInformation.com // http://www.historyofinformation.com/expanded.php?id=485

183

Dalakov G. The Differential Engine of Charles Babbage / History of Computers: hardware, software, internet… // http://history-computer.com/Babbage/DifferentialEngine.html

184

Howe J. (April 1840). “Manufacture of Pins”. American Journal of Science and Arts. 38 (1): Appendix, p. 3 // https://archive.org/stream/mobot31753002152160#page/n225/mode/2up/search/howe

185

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186

Babbage C., Morrision P., Morrison E. (2013). On the Principles and Development of the Calculator and Other Seminal Writings. Dover Publications // https://books.google.ru/books?id=FTXyAAAAQBAJ

187

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188

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189

Waller D. (2016). Iron Men: How One London Factory Powered the Industrial Revolution and Shaped the Modern World. Anthem Press // https://books.google.ru/books?id=zLs1DgAAQBAJ

190

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191

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192

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195

Dalakov G. The differential engine of Pehr-Georg and Edvard Scheutz / History of Computers: hardware, software, internet… // http://history-computer.com/Babbage/NextDifferentialEngines/Scheutz.html

196

Watson I. (2012). The Universal Machine: From the Dawn of Computing to Digital Consciousness. Springer Berlin Heidelberg // https://books.google.ru/books?id=jlmVKZ1psCkC

197

^* Некий студент написал в дипломной работе фразу: «По причине того, что досюда никто не дочитает, сердечник трансформатора рекомендуется сделать из дерева» (вариантов этой байки существует множество: «…выпиливаем турбину из цельного куска дерева, всё равно читать никто не будет» и т. п.).

198

Dalakov G. The differential engine of Pehr-Georg and Edvard Scheutz / History of Computers: hardware, software, internet… // http://history-computer.com/Babbage/NextDifferentialEngines/Scheutz.html

199

Collier B. (1991). Little Engines That Could'Ve: The Calculating Machines of Charles Babbage. Garland Publishing, Inc // http://robroy.dyndns.info/collier/

200

Giudice J. P. (2001). Complejidad y dimensiones en los estudios sobre Babbage: la máquina analítica. Un análisis del fracaso cultural del primer proyecto de calculadora digital programable secuencialmente / Argumentos de Razón Téchnica. No.4 (2001), pp. 13–56 // http://institucional.us.es/revistas/argumentos/4/art_1.pdf

201

Babbage C., Morrision P., Morrison E. (2013). On the Principles and Development of the Calculator and Other Seminal Writings. Dover Publications // https://books.google.ru/books?id=FTXyAAAAQBAJ

202

Dalakov G. The differential engine of Pehr-Georg and Edvard Scheutz / History of Computers: hardware, software, internet… // http://history-computer.com/Babbage/NextDifferentialEngines/Scheutz.html

203

Swade D. D. (2005). The Construction of Charles Babbage's Difference Engine No. 2 / IEEE Annals of the History of Computing, Vol. 27, pp. 70–78 // https://ieeexplore.ieee.org/document/1498720/

204

Babbage printer finally runs (2000) / BBC News // http://news.bbc.co.uk/2/hi/science/nature/710950.stm

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Dalakov G. The Analytical Engine of Charles Babbage / History of Computers: hardware, software, internet… // http://history-computer.com/Babbage/AnalyticalEngine.html

206

Collier B. (1991). Little Engines That Could'Ve: The Calculating Machines of Charles Babbage. Garland Publishing, Inc // http://robroy.dyndns.info/collier/

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Bromley A. G. (1998). Charles Babbage's Analytical Engine, 1838. IEEE Ann. Hist. Comput., 20, 29–45 // http://athena.union.edu/~hemmendd/Courses/cs80/an-engine.pdf

208

Shuttleworth M. (2011). Heron's Inventions. / Explorable.com, Retrieved Sep 03, 2023 // https://explorable.com/heron-inventions

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Keranen R. (2016). Inventions in Computing: From the Abacus to Personal Computers. Cavendish Square Publishing // https://books.google.ru/books?id=BHdmDwAAQBAJ

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Dalakov G. Basile Bouchon / History of Computers: hardware, software, internet… // http://history-computer.com/Dreamers/Bouchon.html

213

Broudy E. (1993). The Book of Looms: A History of the Handloom from Ancient Times to the Present. University Press of New England // https://books.google.ru/books?id=shN5_-W1RzcC

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Dalakov G. Jacques de Vaucanson / History of Computers: hardware, software, internet… // http://history-computer.com/Dreamers/Vaucanson.html

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Keranen R. (2016). Inventions in Computing: From the Abacus to Personal Computers. Cavendish Square Publishing // https://books.google.ru/books?id=BHdmDwAAQBAJ

216

Dalakov G. Joseph-Marie Jacquard / History of Computers: hardware, software, internet… // http://history-computer.com/Dreamers/Jacquard.html

217

Полетаев В. Н. Жаккарда машина / Большая советская энциклопедия. — М.: Советская энциклопедия, 1969–1978 // https://dic.academic.ru/dic.nsf/bse/86903/Жаккарда

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http://www.historicalstatistics.org/Currencyconverter.html

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Dalakov G. Joseph-Marie Jacquard / History of Computers: hardware, software, internet… // http://history-computer.com/Dreamers/Jacquard.html

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Sterling B. (2017). Charles Babbage left a computer program in Turin in 1840. Here it is / Wired, 05.14.2017 // https://www.wired.com/beyond-the-beyond/2017/05/charles-babbage-left-computer-program-turin-1840/

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Menabrea L. F., Babbage C., Lovelace A. K. C., L A. A. (1843). Sketch of the Analytical Engine invented by Charles Babbage … with notes by the translator. Extracted from the 'Scientific Memoirs,' etc. [The translator's notes signed: A.L.L. ie. Augusta Ada King, Countess Lovelace.]. R. & J. E. Taylor // https://books.google.ru/books?id=hPRmnQEACAAJ

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Misa T. J. (2015). Charles Babbage, Ada Lovelace, and the Bernoulli Numbers / Hammerman R., Russell A. L. (2015). Ada's Legacy: Cultures of Computing from the Victorian to the Digital Age. Association for Computing Machinery and Morgan & Claypool // https://doi.org/10.1145/2809523.2809527

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Henry Babbage's Analytical Engine Mill, 1910 // http://collection.sciencemuseum.org.uk/objects/co62246/henry-babbages-analytical-engine-mill-1910-analytical-engine-mills

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Babbage Analytical Engine designs to be digitized / BBC News, 21 September 2011 // https://www.bbc.com/news/technology-15001514

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Babbage’s last laugh / The Economist, 9 September 1999 // https://www.economist.com/science-and-technology/1999/09/09/babbages-last-laugh

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Sheepshanks R. (1860). A letter to the Board of visitors of the Greenwich royal observatory in reply to the calumnies of Mr. Babbage at their meeting in June 1853, and in his book entitled The exposition of 1851. London, Printed by G. Barclay // https://archive.org/details/alettertoboardv00babbgoog/page/n101

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^* В период с 1784 по 1896 г. Колумбийским колледжем назывался будущий Колумбийский университет.

253

Dalakov G. Tabulating machine of Herman Hollerith / History of Computers: hardware, software, internet… // https://history-computer.com/ModernComputer/Basis/TabulatingMachine_Hollerith.html

254

Muirhead J. F. (1963). John Shaw Billings. New England Journal of Medicine, Vol. 268, Iss. 14, pp. 778–779 // https://doi.org/10.1056/nejm196304042681409

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256

Truesdell L. E. (1965). The development of punch card tabulation in the Bureau of the Census, 1890–1940: with outlines of actual tabulation programs. U. S. G.P.O // https://books.google.ru/books?id=MGZqAAAAMAAJ

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258

^* Ин-кварто (лат. in quarto «в четвёртую часть листа», «в четвёртку» от лат. quartus «четвёртый») — полиграфический термин, обозначающий размер страницы в одну четверть типографского листа. На одном листе при этом помещается 4 листа (8 страниц) книги. Размеры страницы составляют 241,5 × 305 мм.

259

Ruggles S., Magnuson D. L. (2018). Capturing the American People: Census Technology and Institutional Change, 1790–2020 / MPC Working Papers Series. №2 // https://pop.umn.edu/sites/pop.umn.edu/files/ruggles_magnuson_capturing-2.pdf

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^* Lieutenant Commander, соответствует званию капитана третьего ранга или армейского майора.

279

Brooks J. (2004). Dreadnought Gunnery and the Battle of Jutland: The Question of Fire Control. Taylor & Francis // https://books.google.ru/books?id=dEmRAgAAQBAJ

280

^** Примерно 1280 м.

281

^*** Примерно 7300 м.

282

^**** Примерно 46,3 км/ч.

283

^* Гиростат (gyrostat) — модифицированный вариант гироскопа. Гироскоп — используемый для автоматического регулирования устойчивости прибор с диском и свободной осью, всегда сохраняющей неизменное положение.

284

^** Примерно 45 м.

285

^*** Примерно 565 м.

286

^**** Примерно 18 200 м.

287

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291

^* Channel Fleet, старейший английский флот, чьей задачей являлась защита Британских островов со стороны Ла-Манша.

292

^* Captain, соответствует званию капитана первого ранга или армейского полковника.

293

^** Commander, соответствует званию капитана второго ранга, или армейского подполковника.

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^* Примерно 13 700 м.

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301

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315

^* Гирокомпас — механический указатель направления истинного (географического) меридиана, предназначенный для определения курса объекта, а также азимута (пеленга) ориентируемого направления. Принцип действия гирокомпаса основан на использовании свойств гироскопа и суточного вращения Земли. Идея гирокомпаса была предложена французским учёным Жаном Фуко.

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^* Маргиттаи означает «из Маргиты», а окончание –i — типичное окончание, используемое при образовании венгерских дворянских имён от названия местности; но семья Неймана не имела никакого отношения к городу Маргита, фамилию старший Нейман, по всей видимости, избрал по имени жены, а на выбранном гербе были изображены три маргаритки на зелёном поле.

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^* В английском языке тут присутствует дополнительная игра слов: earful of beer означает «пивная взбучка», а созвучное ему ear full of beer — «полное ухо пива».

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^* В ряде источников встречается, что он предсказал рост цены акций IBM на 15 пунктов ввиду выхода телевизионного сюжета и оказался прав. Однако более скрупулёзный анализ динамики котировок акций компании свидетельствует о том, что это не более чем миф. В действительности в тот день торговля акциями IBM закрылась с незначительным снижением, а рост котировок в последующие недели происходил со среднерыночными темпами.

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571

^* Duke значит «герцог» и в то же время совпадает с названием университета; шахматная программа, в разработке которой также участвовал Траскотт, называлась Duchess — «герцогиня».

572

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579

^* Здесь и далее я буду использовать мужской род для программ Chinook, Fritz и нескольких других. Формально это неправильно, но фразы типа «Chinook играла» или «Fritz выиграла» звучат неестественно и режут мне слух.

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675

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676

McCarthy J. (2006). The Dartmouth Workshop--as planned and as it happened // http://www-formal.stanford.edu/jmc/slides/dartmouth/dartmouth/node1.html

677

Logan A., Gill B. (1958). Runner-up / The New Yorker, November 29, p. 43 // https://www.newyorker.com/magazine/1958/11/29/runner-up-4

678

McCorduck P. (2004). Machines who think: a personal inquiry into the history and prospects of artificial intelligence. A. K. Peters // https://books.google.ru/books?id=aH9QAAAAMAAJ

679

Википедия // https://ru.wikipedia.org/wiki/Понедельник_начинается_в_субботу (версия от 29.05.2019).

680

OFF-LINE интервью с Борисом Стругацким. Сентябрь 2009 / Аркадий и Борис Стругацкие: официальный сайт // http://www.rusf.ru/abs/int0132.htm

681

Глушкова А., Жабин С. (2019). Виртуальная страна Кибертония — субкультура советских программистов / Спильне. 8 апреля // https://commons.com.ua/uk/virtualnaya-strana-kibertoniya/

682

Игорь Осипчук (2013). Дочь академика Глушкова: «Прочтя 20 страниц математического текста, отец запоминал его наизусть» / Факты // https://fakty.ua/169041-prochtya-20-stranic-matematicheskogo-teksta-otec-zapominal-ego-naizust

683

^* Сегодня слово «хакер» обычно используется для обозначения компьютерных взломщиков, но изначально оно имело иной смысл; хакер — это тот, кто «врубается», компьютерный энтузиаст и эксперт.

684

685

Волошин А. (1965). Кибертония-65 / Вечерний Киев. Суббота 16 янв. С. 2 // http://ogas.kiev.ua/library/kybertonyya-65-694

686

687

688

689

Смилга В. П. (1956). Возможен ли шахматный автомат? / Шахматы в СССР. № 6.

690

Смилга В. П. (1963). Электронный гроссмейстер / Знание — сила. № 12.

691

Евграфов М. А., Задыхайло И. Б. (1965). Некоторые соображения о программировании шахматной игры / Проблемы кибернетики. № 15.

692

Туманов В. «„Лучший ход“ — за 58 секунд» // Таль — Ботвинник: матч-реванш на первенство мира. Бюллетень Центрального шахматного клуба СССР. 1961. № 8. С. 4—5.

693

Ландис Е. М., Яглом И. М. (2001). Об Александре Семёновиче Кронроде / Успехи математических наук. Т. 56, вып. 5(341). С. 191–201 // https://doi.org/10.4213/rm448

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Академия наук СССР, Трапезников В.А., Национальный комитет СССР по автоматическому управлению (1967). Оптимальные системы, статистические методы: труды III Всесоюзного совещания по автоматическому управлению (технической кибернетике), Одесса, 20–26 сентября 1965 г. Наука // https://books.google.ru/books?id=S_fmAAAAMAAJ

697

Nelson W. L. (2011). 1965 USSR Trip / MYBELLLABSDAYS // https://mybelllabsdays.wordpress.com/2011/12/04/1965-ussr-trip/

698

Маккарти Д. (2006). Вспоминая Андрея Петровича Ершова / Андрей Петрович Ершов — учёный и человек / под ред. А. Г. Марчука. — Новосибирск: Издательство Сибирского отделения РАН // https://www.iis.nsk.su/files/book/file/Ershov_kniga.pdf

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Маккарти Д. (1965). Письмо Андрею Ершову / Архив академика А. П. Ершова // http://ershov.iis.nsk.su/node/777848

700

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McCarthy J. (2005). The History of Computer Chess: An AI Perspective (Video). Mountain View, CA, USA: Computer History Museum // https://www.youtube.com/watch?v=AvU_fnLWRRk

702

Кронрод А.С. (2004) Беседы о программировании / Предисл. Л. А. Кронрод. Послесл. В. Л. Арлазарова. Изд. 2-е, стереотипное. — М.: Едиториал УРСС // https://nsu.ru/xmlui/handle/nsu/9050

703

http://greko.su/m20-itef.pdf

704

Рузайкин Г. И., Коновалов С. П. (2005). Рассказ «бабки-повитухи» / Мир ПК. № 10 // https://www.osp.ru/pcworld/2005/10/317270/

705

Адельсон-Вельский Г. М., Арлазаров В. Л., Битман А. Р., Животовский А. А., Усков А. В. (1970). О программировании игры вычислительной машины в шахматы / Успехи математических наук. Т. 25, вып. 2 (152). С. 221—260 // http://mi.mathnet.ru/umn5324

706

Гутер Р. С., Арлазаров В. Л., Усков А. В. (1965). Практика программирования: Справочник. — М.: Наука.

707

Ершов А.П., Лавров С.С., Семендяев К.А. (1966). Письмо в «Литературную газету» / Архив академика А. П. Ершова // http://ershov.iis.nsk.su/node/806835

708

Гутер Р. С., Арлазаров В. Л., Усков А. В. (1965). Практика программирования: Справочник. — М.: Наука.

709

^* Язык ассемблера (assembly language) — язык программирования низкого уровня. Он представляет собой систему обозначений, используемую для представления в удобочитаемой форме программ, записанных в машинном коде. Команды языка соответствуют отдельным командам, выполняемым процессором машины, или их коротким последовательностям. Поскольку наборы команд различаются в зависимости от используемой аппаратной платформы, в действительности мы имеем дело не с единым языком, а с классом аппаратно-специфичных языков, хотя и разделяющих обычно некоторые условные обозначения. Например, команда ADD, используемая для сложения чисел, почти во всех этих языках называется именно так.

710

Костинский А. (2002). Компьютерные программы, как конец спортивных шахмат / Радио Свобода // https://www.svoboda.org/a/24203756.html

711

Berenyi I. (1970). Computers in Eastern Europe / Scientific American, Vol. 223, Iss. 4.

712

Малиновский Б. Н. (1995). История вычислительной техники в лицах. — К.: фирма «КИТ», ПТОО «А.С.К.» // http://lib.ru/MEMUARY/MALINOWSKIJ/0.txt

713

Донской М. История «Каиссы» / Виртуальный компьютерный музей // http://www.computer-museum.ru/games/kaissa1.htm

714

Chess: Ancient precursors and related games / Encyclopædia Britannica. 2002 // https://www.britannica.com/topic/chess

715

Chalmers A., Johnson S. (1810). The Works of the English Poets, from Chaucer to Cowper: Including the Series Edited with Prefaces, Biographical and Critical. J. Johnson // https://books.google.ru/books?id=b0LVAAAAMAAJ

716

Murray H. J. R. (2015). A History of Chess. Skyhorse Publishing // https://books.google.ru/books?id=dNSBCgAAQBAJ

717

Донской М. История «Каиссы» / Виртуальный компьютерный музей // http://www.computer-museum.ru/games/kaissa1.htm

718

Müller K., Schaeffer J. (2018). Man Vs. Machine: Challenging Human Supremacy at Chess. New York, NY, USA: Russell Enterprises, Incorporated // https://books.google.ru/books?id=0GV2DwAAQBAJ

719

Донской М. История «Каиссы» / Виртуальный компьютерный музей // http://www.computer-museum.ru/games/kaissa1.htm

720

Wall B. Kaissa // http://billwall.phpwebhosting.com/articles/Kaissa.htm

721

^* Первый разряд соответствует силе игры в 1800–2000 пунктов Эло, рейтинг Эло — метод расчёта относительной силы игроков в играх с двумя игроками; эту систему рейтингов разработал американский профессор физики венгерского происхождения Арпад Эло; новичкам соответствует рейтинг Эло 1000–1200, разница в 100 пунктов между двумя игроками означает, что сильнейший игрок набирает в среднем 64% очков, разница в 200 пунктов — 76% очков.

722

^** Архитектура машины позволяла выполнять быстрые операции с 64-разрядными целыми числами, в которых каждый разряд соответствует одному из полей шахматной доски; сегодня эти технологии называются bitboards — дословно «битовые доски»; впервые этот подход предложил ещё Шура-Бура.

723

Владимир Арлазаров: Персона дня — 19.10.2018 / Российская Шахматная Федерация // https://ruchess.ru/persons_of_day/vladimir_arlazarov_pd/?sphrase_id=180658

724

Computer chess pioneer Mikhail Donskoy passes on // https://en.chessbase.com/post/computer-che-pioneer-mikhail-donskoy-paes-on

725

3rd World Computer Chess Championship / ICGA Tournaments: Tournaments between computer programs: chess, draughts, checkers, Go, backgammon, and more // https://www.game-ai-forum.org/icga-tournaments/tournament.php?id=68

726

Reseña histórica del ajedrez por computadora (VI) // http://www.anacadigital.com/historia/anaca5_1_89.htm

727

Horváth Z. (1990). Report on the 2nd Computer Olympiad. ICCA Journal, Vol. 13, No. 3.

728

2nd Computer Olympiad, Chess / ICGA Tournaments: Tournaments between computer programs: chess, draughts, checkers, Go, backgammon, and more // https://www.game-ai-forum.org/icga-tournaments/tournament.php?id=142

729

Костинский А. (2002). Компьютерные программы как конец спортивных шахмат / Радио Свобода // https://www.svoboda.org/a/24203756.html

730

Арлазаров В. Л., Битман А. Р. (1968). Обыграет ли машина человека? / Шахматы в СССР. № 2. С. 9—11.

731

Адельсон-Вельский Г. М., Арлазаров В. Л., Битман А. Р., Животовский А. А., Усков А. В. (1969). О программировании шахматной игры / Труды первой зимней школы по математическому программированию. Вып. II. С. 216—252.

732

733

Adelson-Velsky G., Arlazarov V., Donskoy M. (1975). Some Methods of Controlling the Tree Search in Chess Programs. Artificial Ingelligence, Vol. 6, No. 4, pp. 361–371.

734

Adelson-Velsky G., Arlazarov V., Donskoy M. (1977). On the Structure of an Important Class of Exhaustive Problems and Methods of Search Reduction for them. Advances in Computer Chess 1

735

Адельсон-Вельский Г. М., Арлазаров В. Л., Битман А. Р., Донской М. В. (1983). Машина играет в шахматы. — М.: Наука // http://www.computer-museum.ru/books/kaissa.pdf

736

Haugeland J. (1985). Symbolic Computation. Artificial Intelligence: The Very Idea. MIT Press // https://books.google.ru/books?id=UuQbnAEACAAJ

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738

Carrera P., Cherubino G., Tortelli M., Rossi G. d., Romano G. (1617). Il gioco de gli scacchi di D. Pietro Carrera diuiso in otto libri, ne' quali s'insegnano i precetti, le vscite, e i tratti posticci del gioco, e si discorre della vera origine di esso. Con due discorsi, l'vno del padre D. Gio. Battista Chèrubino, l'altro del dottor Mario Tortelli, opera non meno vtile a' professori del gioco, che diletteuole a' gli studiosi per la varieta' della eruditione cauata dalle tenebre dell'antichita'. per Giouanni de' Rossi da Trento // https://books.google.ru/books?id=RPvGROWRIikC

739

Lolli G. (1763). Osservazioni teorico-pratiche sopra il giuoco degli scacchi ossia il Giuoco degli Scacchi: esposto nel sus miglian lume. Stamp. di S. Tommaso d'Aquino // https://books.google.ru/books?id=zych5drFRuQC

740

Ботвинник М. (1979). От шахматиста — к машине. М.: Физкультура и спорт // https://books.google.ru/books?id=W8aptgEACAAJ

741

Phony Benoni. Wageningen Caltex (1958) / Chessgames.com: online chess database and community // http://www.chessgames.com/perl/chesscollection?cid=1026124

742

Мюннингхофф А. (1979). Макс Эйве / Пер. с нидерландского В. И. Мурахвери — М.: Физкультура и спорт.

743

^* Европейское сообщество по атомной энергии.

744

O'Connor J. J., Robertson E. F. (2003). Machgielis Euwe. School of Mathematics and Statistics University of St Andrews // http://www-history.mcs.st-andrews.ac.uk/history/Biographies/Euwe.html

745

Ботвинник М. (1979). От шахматиста — к машине. М.: Физкультура и спорт // https://books.google.ru/books?id=W8aptgEACAAJ

746

Ботвинник М. М. (1961). Люди и машины за шахматной доской / Шахматы в СССР. № 3.

747

Жанна Михайловна Таль, персональные коммуникации.

748

В шахматы «играет» ЭВМ. Телевизионные новости. Эфир 24.11.1968 // https://www.youtube.com/watch?v=LZEd6ZtSxCo

749

Goodman R., Soni J. (2017). The Man Who Built The Chess Machine / Chess.com // https://www.chess.com/article/view/the-man-who-built-the-chess-machine

750

Ботвинник, М. (1979). От шахматиста — к машине. М.: Физкультура и спорт // https://books.google.ru/books?id=W8aptgEACAAJ

751

Ботвинник М. М. (1966). Математическое отображение шахматной игры (Пособие для шахматного анализа) / Бюллетень центрального шахматного клуба СССР. № 3.

752

Кухарева А. (2003). Михаил Донской: Я Билла Гейтса ни в чем не виню. ИД «Компьютерра, 2003. Сайт «Домашний компьютер» — приложение к интернет-изданию «Компьюлента» / Сайт Александра Тимофеева // http://atimopheyev.narod.ru/AfterPIONEER/info/PIONEER/2.htm

753

Карпов А. (2022). «Мальчик понятия не имеет о шахматах». Гроссмейстер Карпов — о школе, первых деньгах и знакомстве с Ботвинником / Мел, 25.01.2022 // https://mel.fm/zhizn/knigi/4218760-malchik-ponyatiya-ne-imeyet-o-shakhmatakh-grossmeyster-karpov--o-shkole-pervykh-dengakh-i-znakomstve

754

Botvinnik M., Brown A. (1970). Computers, chess and long-range planning. Springer-Verlag // https://books.google.ru/books?id=ZYxRAAAAMAAJ

755

Ботвинник М. М. (1968). Алгоритм игры в шахматы. — М. // http://whychess.ru/776algoritm-igru-v-shahmatu.html

756

Книжник С. (2009). Наставник для компьютера / Наука в Сибири. № 17 (2702), 30 апреля // http://www.nsc.ru/HBC/hbc.phtml?5+500+1

757

Ботвинник М. М. (1987). Аналитические и критические работы. Статьи и воспоминания // http://whychess.ru/botvinnik-stati-vospominaniua.html

758

Ботвинник, М. (1979). От шахматиста — к машине. М.: Физкультура и спорт // https://books.google.ru/books?id=W8aptgEACAAJ

759

^* Этюд Рети — знаменитый этюд (белые: Крh8, пешка с6, чёрные: Крa6, пешка h5), в котором используется неевклидова геометрия шахматной доски: движение короля по диагонали занимает столько же ходов, сколько движение по прямой.

760

^** Сила игры международного мастера соответствует 2400–2500 пунктов Эло, к 1981 г. звание «международный мастер» было присвоено 897 шахматистам.

761

Lopez R., Sentef J. (2017). Comments / Marginal Revolution // https://marginalrevolution.com/marginalrevolution/2017/03/new-george-steiner-book.html

762

^*** Рейтинг Эло свыше 2500, в 1988 г. в мире было 338 международных гроссмейстеров.

763

Lieserson C., Newborn M. (2013). Deep Blue: An Artificial Intelligence Milestone. Springer New York // https://books.google.ru/books?id=rWPgBwAAQBAJ

764

^* По неведомым причинам в советских научно-популярных изданиях её именовали на славянский манер — «Хитеч».

765

^* Миттельшпиль (от нем. Mittelspiel — середина игры) — следующая за дебютом стадия шахматной партии, в которой обычно происходят основные события.

766

^** Эндшпиль (от нем. Endspiel — «заключительная игра») — заключительная часть шахматной партии, после размена большинства фигур.

767

Berliner H. J. (1977). Experiences in Evaluation with BKG, a Program That Plays Backgammon / Proceedings of IJCAI, 1977 (1979), pp. 428–433 // http://www.bkgm.com/articles/Berliner/ExperiencesInEvaluationWithBKG/index.html

768

Berliner H. J. (1980). Backgammon Computer Program Beats World Champion / Artificial Intelligence, vol. 14 (1980), pp. 205—220 // http://www.bkgm.com/articles/Berliner/BackgammonProgramBeatsWorldChamp/

769

Hsu F. (2004). Behind Deep Blue: Building the Computer that Defeated the World Chess Champion. Princeton University Press // https://books.google.ru/books?id=WOk9DwAAQBAJ

770

Hsu F. (2004). Behind Deep Blue: Building the Computer that Defeated the World Chess Champion. Princeton University Press // https://books.google.ru/books?id=WOk9DwAAQBAJ

771

Theo van der Storm (2002). North American Computer-Chess Championships: Complete History of Tournament Results and Games // https://old.csvn.nl/ncc_hist.html#17th

772

Atkinson G. (1998). Chess and Machine Intuition. Intellect Books // https://books.google.ru/books?id=ZuTvVo4zo6oC

773

6th World Computer Chess Championship / ICGA Tournaments: Tournaments between computer programs: chess, draughts, checkers, Go, backgammon, and more // https://www.game-ai-forum.org/icga-tournaments/tournament.php?id=14

774

All Time Rankings / Edinburgh University Chess Club Home Page // https://web.archive.org/web/20100724043700/http://chess.eusa.ed.ac.uk/Chess/Trivia/AlltimeList.html

775

Hsu F. (2004). Behind Deep Blue: Building the Computer that Defeated the World Chess Champion. Princeton University Press // https://books.google.ru/books?id=WOk9DwAAQBAJ

776

Volker Jeschonnek (2000). A Visit to My Opponent's Camp: Introducing Man vs. Machine (CC) Challenges and Wchess / Ralph Marconi Chess Page // https://web.archive.org/web/20001218000600/http://correspondencechess.com/marconi/volkerart.htm

777

Kasparov versus Deep Thought documentary / PBS Nova // https://www.youtube.com/watch?v=mhnDzk9IVAA

778

Hsu F. (2004). Behind Deep Blue: Building the Computer that Defeated the World Chess Champion. Princeton University Press // https://books.google.ru/books?id=WOk9DwAAQBAJ

779

Laswon D. (1989). The Pentagon plays chess / The Spectator, 28 Janyary 1989, p. 9 // http://archive.spectator.co.uk/article/28th-january-1989/9/the-pentagon-plays-chess

780

Krauthammer C. (1989). Checkmated by a monster of calculation / The Washington Post, 24 February 1989 // https://www.washingtonpost.com/archive/opinions/1989/02/24/checkmated-by-a-monster-of-calculation/9afad6af-939b-4c6a-8641-7cf5016f2cd5/

781

^* «Глубокая глотка» — это кодовое имя информатора журналистов-расследователей из The Washington Post в ходе Уотергейтского скандала, а также название фильма, на просмотр которого не стоит приглашать свою маму.

782

Hsu F. (2004). Behind Deep Blue: Building the Computer that Defeated the World Chess Champion. Princeton University Press // https://books.google.ru/books?id=WOk9DwAAQBAJ

783

Lieserson C., Newborn M. (2013). Deep Blue: An Artificial Intelligence Milestone. Springer New York // https://books.google.ru/books?id=rWPgBwAAQBAJ

784

Hsu F. (2004). Behind Deep Blue: Building the Computer that Defeated the World Chess Champion. Princeton University Press // https://books.google.ru/books?id=WOk9DwAAQBAJ

785

Lieserson C., Newborn M. (2013). Deep Blue: An Artificial Intelligence Milestone. Springer New York // https://books.google.ru/books?id=rWPgBwAAQBAJ

786

Theo van der Storm (2002). North American Computer-Chess Championships: Complete History of Tournament Results and Games // https://old.csvn.nl/ncc_hist.html#22th

787

Jiu H. (1993). P. C. CORNER // https://www.thecrimson.com/article/1993/11/9/p-c-corner-pwhen-current-us

788

Lieserson C., Newborn M. (2013). Deep Blue: An Artificial Intelligence Milestone. Springer New York // https://books.google.ru/books?id=rWPgBwAAQBAJ

789

Theo van der Storm. Harvard Cup Human vs. Computer Chess Challenge // https://old.csvn.nl/harvhist.html#4th

790

8th World Computer Chess Championship / ICGA Tournaments: Tournaments between computer programs: chess, draughts, checkers, Go, backgammon, and more // https://www.game-ai-forum.org/icga-tournaments/tournament.php?id=29

791

Lieserson C., Newborn M. (2013). Deep Blue: An Artificial Intelligence Milestone. Springer New York // https://books.google.ru/books?id=rWPgBwAAQBAJ

792

Newborn M. (2012). Kasparov versus Deep Blue: Computer Chess Comes of Age. Springer New York // https://books.google.ru/books?id=IiXjBwAAQBAJ

793

All Time Rankings / Edinburgh University Chess Club Home Page // https://web.archive.org/web/20100724043700/http://chess.eusa.ed.ac.uk/Chess/Trivia/AlltimeList.html

794

Lieserson C., Newborn M. (2013). Deep Blue: An Artificial Intelligence Milestone. Springer New York // https://books.google.ru/books?id=rWPgBwAAQBAJ

795

Müller K., Schaeffer J. (2018). Man Vs. Machine: Challenging Human Supremacy at Chess. New York, NY, USA: Russell Enterprises, Incorporated // https://books.google.ru/books?id=0GV2DwAAQBAJ

796

Antonoff M. (1996). Curtains for Kasparov? / Popular Science. №3, 1996 // https://books.google.ru/books?id=-TKv7UHgoTQC&pg=PA43

797

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798

Isenberg G. (2018). Frans Morsch / Chess Programming Wiki // https://www.chessprogramming.org/Frans_Morsch

799

Гниренко В. (2012). Рекорды двух символических клубов / Шахматное обозрение. №1.

800

Jones B. (2007). Grandmaster Maurice Ashley comes to Baltimore, playing chess – and teacher / The Baltimore Sun, October 3 // https://www.baltimoresun.com/news/bs-xpm-2007-10-03-0710030152-story.html

801

Lieserson C., Newborn M. (2013). Deep Blue: An Artificial Intelligence Milestone. Springer New York // https://books.google.ru/books?id=rWPgBwAAQBAJ

802

^* HAL 9000 — вымышленный компьютер из цикла произведений «Космическая одиссея» Артура Кларка, обладающий способностью к самообучению и являющийся примером искусственного интеллекта в научной фантастике; поскольку HAL вступил в конфликт с людьми, его образ нередко использовался в качестве архетипического «злого ИИ».

803

Gaulin E. (1996). Computer 1, chess champion 0 / Atlanta Journal and Constitution, February 11.

804

Hsu F. (2004). Behind Deep Blue: Building the Computer that Defeated the World Chess Champion. Princeton University Press // https://books.google.ru/books?id=WOk9DwAAQBAJ

805

Lieserson C., Newborn M. (2013). Deep Blue: An Artificial Intelligence Milestone. Springer New York // https://books.google.ru/books?id=rWPgBwAAQBAJ

806

Hsu F. (2004). Behind Deep Blue: Building the Computer that Defeated the World Chess Champion. Princeton University Press // https://books.google.ru/books?id=WOk9DwAAQBAJ

807

Lieserson C., Newborn M. (2013). Deep Blue: An Artificial Intelligence Milestone. Springer New York // https://books.google.ru/books?id=rWPgBwAAQBAJ

808

^* Сотрудник автоинспекции тормозит всех подряд и задаёт один и тот же вопрос:

— Если я у тебя свечу выкручу, какое колесо спустит?

— Не знаю…

— Не знаешь правил — плати штраф!

И так весь день, пока не остановил «запорожец»:

— Если я у тебя свечу выкручу, какое колесо спустит?

— А если я тебе монтировкой по голове ударю, какой шнурок развяжется?

809

Hsu F. (2004). Behind Deep Blue: Building the Computer that Defeated the World Chess Champion. Princeton University Press // https://books.google.ru/books?id=WOk9DwAAQBAJ

810

Lieserson C., Newborn M. (2013). Deep Blue: An Artificial Intelligence Milestone. Springer New York // https://books.google.ru/books?id=rWPgBwAAQBAJ

811

Hsu F. (2004). Behind Deep Blue: Building the Computer that Defeated the World Chess Champion. Princeton University Press // https://books.google.ru/books?id=WOk9DwAAQBAJ

812

Lieserson C., Newborn M. (2013). Deep Blue: An Artificial Intelligence Milestone. Springer New York // https://books.google.ru/books?id=rWPgBwAAQBAJ

813

Waga P. (1996). Kasparov, IBM plan man vs. machine rematch / The Reporter Dispatch, Gannett Suburban Newspapers, August 21,1996.

814

Saylor M. (1997). Computers cast a long shadow on chessboard / Los Angeles Times, May 1 // https://www.latimes.com/archives/la-xpm-1997-05-01-mn-54193-story.html

815

Antonoff M. (1997). Game, net & match / Yahoo Internet Life, May.

816

Kasparov challenger receives an upgrade / The New York Times, May 1, 1997.

817

Kim J. (1997). More than just chess. But not as simple as man vs.computer / USA Today, May 2.

818

Hsu F. (2004). Behind Deep Blue: Building the Computer that Defeated the World Chess Champion. Princeton University Press // https://books.google.ru/books?id=WOk9DwAAQBAJ

819

Lieserson C., Newborn M. (2013). Deep Blue: An Artificial Intelligence Milestone. Springer New York // https://books.google.ru/books?id=rWPgBwAAQBAJ

820

Lieserson C., Newborn M. (2013). Deep Blue: An Artificial Intelligence Milestone. Springer New York // https://books.google.ru/books?id=rWPgBwAAQBAJ

821

Hsu F. (2004). Behind Deep Blue: Building the Computer that Defeated the World Chess Champion. Princeton University Press // https://books.google.ru/books?id=WOk9DwAAQBAJ

822

Chess Opening Explorer / 365Chess.com: Biggest Chess Games Database Online // https://www.365chess.com/opening.php

823

Müller K., Schaeffer J. (2018). Man Vs. Machine: Challenging Human Supremacy at Chess. New York, NY, USA: Russell Enterprises, Incorporated // https://books.google.ru/books?id=0GV2DwAAQBAJ

824

Ingo Althoefer vs Deep Thought (Computer), Hanover (1991). Mieses Opening: Reversed Rat (A00), 0-1 / chessgames.com: online chess database and community // http://www.chessgames.com/perl/chessgame?gid=1472153

825

Althöfer I. (2013). Random Structures from Lego Bricks and Analog Monte Carlo Procedures // https://www.althofer.de/random-lego-structures.pdf

826

Hsu F. (2004). Behind Deep Blue: Building the Computer that Defeated the World Chess Champion. Princeton University Press // https://books.google.ru/books?id=WOk9DwAAQBAJ

827

Bonesteel, Mark (2017). Diego Maradona admits video replay would have erased his 'Hand of God' goal / The Washington Post, 26 July // https://www.washingtonpost.com/news/early-lead/wp/2017/07/26/diego-maradona-admits-video-replay-would-have-erased-his-hand-of-god-goal/

828

^* Этот гол вошёл в историю мирового футбола под названием «рука Бога», поскольку на послематчевой конференции автор гола заявил, что спорный гол был забит «отчасти головой Марадоны, а отчасти рукой Бога».

829

Weber B. (1996). Chess Computer Seeking Revenge Against Kasparov / New York Times, August 20 // https://www.nytimes.com/1996/08/20/nyregion/chess-computer-seeking-revenge-against-kasparov.html

830

Kasparov G., Greengard M. (2017). Deep Thinking: Where Machine Intelligence Ends and Human Creativity Begins. John Murray Press // https://books.google.ru/books?id=ffYZDQAAQBAJ

831

Hsu F. (2004). Behind Deep Blue: Building the Computer that Defeated the World Chess Champion. Princeton University Press // https://books.google.ru/books?id=WOk9DwAAQBAJ

832

Kasparov G., Greengard M. (2017). Deep Thinking: Where Machine Intelligence Ends and Human Creativity Begins. John Murray Press // https://books.google.ru/books?id=ffYZDQAAQBAJ

833

Hsu F. (2004). Behind Deep Blue: Building the Computer that Defeated the World Chess Champion. Princeton University Press // https://books.google.ru/books?id=WOk9DwAAQBAJ

834

Kasparov G., Greengard M. (2017). Deep Thinking: Where Machine Intelligence Ends and Human Creativity Begins. John Murray Press // https://books.google.ru/books?id=ffYZDQAAQBAJ

835

Hsu F. (2004). Behind Deep Blue: Building the Computer that Defeated the World Chess Champion. Princeton University Press // https://books.google.ru/books?id=WOk9DwAAQBAJ

836

Kasparov G., Greengard M. (2017). Deep Thinking: Where Machine Intelligence Ends and Human Creativity Begins. John Murray Press // https://books.google.ru/books?id=ffYZDQAAQBAJ

837

Deep Blue (Computer) vs Garry Kasparov, New York (1997). Caro-Kann Defense: Karpov. Modern Variation (B17), 1-0 / chessgames.com: online chess database and community // http://www.chessgames.com/perl/chessgame?gid=1070917

838

Kasparov G., Greengard M. (2017). Deep Thinking: Where Machine Intelligence Ends and Human Creativity Begins. John Murray Press // https://books.google.ru/books?id=ffYZDQAAQBAJ

839

Dirk Jan ten Geuzendam (2009). Interview: Miguel Illescas / New In Chess magazine. № 5.

840

Kasparov G., Greengard M. (2017). Deep Thinking: Where Machine Intelligence Ends and Human Creativity Begins. John Murray Press // https://books.google.ru/books?id=ffYZDQAAQBAJ

841

Hoffman P. (2003). Retooling Machine and Man For Next Big Chess Faceoff / The New York Times, Jan. 21 // https://www.nytimes.com/2003/01/21/science/retooling-machine-and-man-for-next-big-chess-faceoff.html

842

Rebel vs Yusupov // http://www.rebel.nl/italy.htm

843

Rebel vs Anand // http://www.rebel.nl/anand.htm

844

Kramnik-Deep Fritz match ends in 4-4 draw! / The Chess Drum // https://www.thechessdrum.net/newsbriefs/2002/NB_BrainGames2.html

845

An interview with world chess champion Vladimir Kramnik on Man vs Machine and Classical World Championships // https://en.chessbase.com/post/vladimir-kramnik-on-man-vs-machine-and-world-championships

846

Shabazz D. (2003). Kasparov & Deep Junior fight 3–3 to draw! / The Chess Drum // https://www.thechessdrum.net/tournaments/Kasparov-DeepJr/

847

Biever C. (2003). Kasparov 'forced' to draw with X3D Fritz / New Scientist, 12 November // https://www.newscientist.com/article/dn4376-kasparov-forced-to-draw-with-x3d-fritz/

848

Bilbao Man vs Machine – a resume // https://en.chessbase.com/post/bilbao-man-vs-machine-a-resume

849

8:4 final score for the machines – what next? // https://en.chessbase.com/post/8-4-final-score-for-the-machines-what-next-

850

Adams vs Hydra: Man 0.5 – Machine 5.5 // https://en.chessbase.com/post/adams-vs-hydra-man-0-5-machine-5-5

851

Schulz A. (2006). Kramnik gegen Deep Fritz: Das letzte Match Mensch gegen Maschine? / Spiegel Online // https://www.spiegel.de/netzwelt/tech/kramnik-gegen-deep-fritz-das-letzte-match-mensch-gegen-maschine-a-450147.html

852

^* Термин «движок» требует некоторых объяснений. В 2000-е годы окончательно закрепилось разделение шахматных программ на две независимые части — «оболочку» (Graphic User Interface, GUI) и «движок» (engine), связанные между собой при помощи одного из стандартных интерфейсов, например WinBoard или UCI (Universal Chess Interface). Эта практика возникла в 1990-е годы в продуктах ChessBase, в которых оболочка от ChessBase поставлялась с несколькими шахматными движками, такими как Fritz, Junior, Shredder, Hiarcs, связанными с оболочкой при помощи программного интерфейса. Затем эта практика была перенята и остальной частью сообщества компьютерных шахмат. Теперь шахматные программисты могли не тратить время на разработку собственного интерфейса, а сосредоточиться на создании «шахматного мозга» программы, сконцентрированного в её движке. Стандартизация интерфейсов шахматных движков позволила автоматизировать проведение матчей и турниров между шахматными программами, исключив из процесса человека. Теперь движки могли обмениваться ходами внутри единой оболочки, которая выполняла роль своеобразного рефери, наблюдая за расходованием времени, корректностью ходов и при необходимости присуждая результаты игры в очевидных ситуациях. Кроме того, оболочке могли быть переданы некоторые дополнительные функции, например выбор ходов из дебютной библиотеки, что позволяло, например, устраивать турниры программ с одинаковой библиотекой дебютов у всех участников.

853

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854

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855

Kaufman L. (2008). The Dzindzi – Rybka 3 Handicap Match // https://en.chessbase.com/post/the-dzindzi-rybka-3-handicap-match

856

Kaufman L. (2008). The Milov vs. Rybka Handicap Match // https://en.chessbase.com/post/the-milov-vs-rybka-handicap-match

857

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858

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859

Tahan M. (1993). The Man Who Counted: A Collection of Mathematical Adventures. New York: W. W. Norton & Co., pp. 113—115 // https://books.google.ru/books?id=WMv_2aSlXOoC&pg=PA113

860

Crops (2017) / FAOSTAT. Retrieved 2019-08-18. Countries - Select All; Regions - World + (Total); Elements - Production Quantity; Items - Wheat; Years – 2017 // http://www.fao.org/faostat/en/#data/QC/

861

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863

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864

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865

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866

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868

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869

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871

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877

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878

^* Скорее всего, этот показатель будет немного улучшен с выходом GPU семейства Hopper-Next от Nvidia в 2024 году.

879

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881

^** Максимальная частота глобального иерархического дерева тактовых импульсов; сеть распределения тактовых импульсов (или дерево тактовых импульсов, когда эта сеть формирует дерево) — часть электрической схемы, которая распределяет сигнал(ы) тактовых импульсов (т. е. импульсов, предназначенных для синхронизации различных процессов в схеме) от общего источника до всех элементов, которые в них нуждаются.

882

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883

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884

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885

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886

Wedd N. (2016). Human-Computer Go Challenges / computer-go.info // http://www.computer-go.info/h-c/index.html

887

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911

^* Реверс-инжиниринг — так в технике и программировании называют исследование некоторого устройства или программы, а также сопроводительной документации в целях обнаружения недокументированных возможностей, изменения исходной системы или её воспроизводства без прямого копирования.

912

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917

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918

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Hallion R. (2003). Taking Flight: Inventing the Aerial Age, from Antiquity Through the First World War. Oxford University Press, USA // https://books.google.ru/books?id=YRqV_PayIKIC

920

Opdycke L. (1999). French Aeroplanes Before the Great War. Schiffer Pub // https://books.google.ru/books?id=qAAKAQAAMAAJ

921

Crouch T. D. (2019). Ader Éole / Encyclopædia Britannica // https://www.britannica.com/topic/Ader-Eole

922

Suddath C. (2010). A Brief History of: Velcro / Time, Tuesday, June 15, 2010 // http://content.time.com/time/nation/article/0,8599,1996883,00.html

923

Brian D., Bharat B. (2010). Shark-skin surfaces for fluid-drag reduction in turbulent flow: a review / Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 28 October 2010 // http://doi.org/10.1098/rsta.2010.0201

924

Mann E. E., Mettetal M. R., May R. M., Drinker M. C., Stevenson B. C., Baiamonte V. L., Marso J. M., Dannemiller E. A., Parker A. E., Reddy S. T., Sande M. K. (2014). Surface Micropattern Resists Bacterial Contamination Transferred by Healthcare Practitioners / Journal of Microbiology & Experimentation, Vol. 1, Iss. 5 // https://www.sharklet.com/2015/01/19/surface-micropattern-resists-bacterial-contamination-transferred-by-healthcare-practitioners-2014/

925

Elliott D. (2009). Designing packs as nature intended / PackagingNews, 30 September 2009 // https://www.packagingnews.co.uk/news/designing-packs-as-nature-intended-30-09-2009

926

Mahdavi A., Ferreira L., Sundback C., Nichol J. W., Chan E. P., Carter D. J. D., Bettinger C. J., Patanavanich S., Chignozha L., Ben-Joseph E., Galakatos A., Pryor H., Pomerantseva I., Masiakos P. T., Faquin W., Zumbuehl A., Hong S., Borenstein J., Vacanti J., Langer R., Karp J. M. (2008). A biodegradable and biocompatible gecko-inspired tissue adhesive / Proceedings of the National Academy of Sciences of the United States of America, Vol. 105, Iss. 7, pp. 2307—2312 // https://doi.org/10.1073/pnas.0712117105

927

Kastrup C. J., Nahrendorf M., Figueiredo J. L., Lee H., Kambhampati S., Lee T., Cho S.-W., Gorbatov R., Iwamoto Y., Dang T. T., Dutta P., Yeon J. H., Cheng H., Pritchard C. D., Vegas A. J., Siegel C. D., MacDougall S., Okonkwo M., Thai A., Stone J. R., Coury A. J., Weissleder R., Langer R., Anderson D. G. (2012). Painting blood vessels and atherosclerotic plaques with an adhesive drug depot / Proceedings of the National Academy of Sciences, Dec 26, 2012 // https://www.pnas.org/content/early/2012/12/10/1217972110

928

^* Адгезив — вещество, способное соединять материалы путём поверхностного сцепления.

929

Li Y., Huang X. J., Heo S. H., Li C. C., Choi Y. K., Cai W. P., Cho S. O. (2007). Superhydrophobic Bionic Surfaces with Hierarchical Microsphere/SWCNT Composite Arrays / Langmuir 2007, Vol. 23, Iss. 4, pp. 2169—2174, November 16, 2006 // https://doi.org/10.1021/la0620758

930

Potyrailo R. A., Bonam R. K., Hartley J. G., Starkey T. A., Vukusic P., Vasudev M., Bunning T., Naik R. R., Tang Z., Palacios M. A., Larsen M., Le Tarte L. A., Grande J. C., Zhong S., Deng T. (2015). Towards outperforming conventional sensor arrays with fabricated individual photonic vapour sensors inspired by Morpho butterflies / Nature Communications, Vol. 6, Article number: 7959 // https://www.nature.com/articles/ncomms8959

931

Platé N. (1993). Liquid-Crystal Polymers. Springer US // https://books.google.ru/books?id=a3wjjzrmwf0C

932

Spencer N. J., Hu H. (2020). Enteric nervous system: sensory transduction, neural circuits and gastrointestinal motility / Nature Reviews Gastroenterology & Hepatology, Vol. 17, pp. 338—351 // https://doi.org/10.1038/s41575-020-0271-2

933

Banhey J., von Bartheld C. S. (2017). The Cellular Composition and Glia-Neuron Ratio in the Spinal Cord of a Human and a Non-Human Primate: Comparison with other Species and Brain Regions / Anatomical record (Hoboken, N. J.), Vol. 301, Iss. 4, pp. 697—710 // https://doi.org/10.1002/ar.23728

934

Huffman C. (2017). Alcmaeon / Stanford Encyclopedia of Philosophy // https://plato.stanford.edu/entries/alcmaeon/

935

Elhadi A. M., Kalb S., Perez-Orribo L., Little A. S., Spetzler R. F., Preul M. C. (2012). The Journey of Discovering Skull Base Anatomy in Ancient Egypt and the Special Influence of Alexandria / Neurosurgical Focus, Vol. 33, Iss. 2:e2 // https://www.medscape.com/viewarticle/769263_6

936

Hobbes T. (1969). Leviathan, 1651. Scolar P // https://books.google.ru/books?id=8xyDAAAAMAAJ

937

Kulstad M., Carlin L. (2013). Leibniz’s Philosophy of Mind / Stanford Encyclopedia of Philosophy // https://plato.stanford.edu/entries/leibniz-mind/

938

Robbins J. (2008). A Symphony in the Brain: The Evolution of the New Brain Wave Biofeedback. Grove Press // https://books.google.ru/books?id=ucRimHppmv0C

939

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940

Robbins J. (2008). A Symphony in the Brain: The Evolution of the New Brain Wave Biofeedback. Grove Press // https://books.google.ru/books?id=ucRimHppmv0C

941

Niedermeyer E., Silva F. d. (2005). Electroencephalography: Basic Principles, Clinical Applications, and Related Fields. Lippincott Williams & Wilkins // https://books.google.ru/books?id=tndqYGPHQdEC

942

Robbins J. (2008). A Symphony in the Brain: The Evolution of the New Brain Wave Biofeedback. Grove Press // https://books.google.ru/books?id=ucRimHppmv0C

943

Niedermeyer E., Silva F. d. (2005). Electroencephalography: Basic Principles, Clinical Applications, and Related Fields. Lippincott Williams & Wilkins // https://books.google.ru/books?id=tndqYGPHQdEC

944

Yamada T., Meng E. (2009). Practical Guide for Clinical Neurophysiologic Testing: EEG. Lippincott Williams & Wilkins // https://books.google.ru/books?id=VdsUrV8jeZ4C

945

Beck A. (1890). Die Bestimmung der Localisation des Gehirn — und Rückenmarksfunctionen vermittelst der electrischen Erscheinungen / Zentralblatt für Physiologie, Vol. 4, pp. 473—476 // https://www.biodiversitylibrary.org/item/49793#page/481/mode/1up

946

Yamada T., Meng E. (2009). Practical Guide for Clinical Neurophysiologic Testing: EEG. Lippincott Williams & Wilkins // https://books.google.ru/books?id=VdsUrV8jeZ4C

947

Jabr F. (2012). Know Your Neurons: The Discovery and Naming of the Neuron / Scientific American, May 14, 2012 // https://blogs.scientificamerican.com/brainwaves/know-your-neurons-the-discovery-and-naming-of-the-neuron/

948

The Nobel Prize in Physiology or Medicine 1906 / NobelPrize.org, Nobel Media AB 2020, 6 May 2020 // https://www.nobelprize.org/prizes/medicine/1906/summary/

949

Douglas Fields R. (2017). Why the First Drawings of Neurons Were Defaced / Quantamagazine, September 28, 2017 // https://www.quantamagazine.org/why-the-first-drawings-of-neurons-were-defaced-20170928/

950

^* Исторически анатомы подразделяли ткани мозга на серое вещество (лат. substantia grisea) и белое вещество (лат. substantia alba), руководствуясь цветом соответствующих тканей. Их цветовая дифференциация обусловлена белым цветом миелина и серым цветом кровеносных капилляров и клеточных тел.

951

Borch-Jacobsen M. (2012). Ernst Fleischl von Marxow (1846-1891): Freud's first therapeutic blunder and how he lied about it / Psychology Today, Feb 07, 2012 // https://www.psychologytoday.com/intl/blog/freuds-patients-serial/201202/ernst-fleischl-von-marxow-1846-1891

952

Medwed H. (1997). Ernst Fleischl von Marxow (1846—1891): Leben und Werk. Medienverl. Köhler // https://books.google.ru/books?id=lGB4AAAACAAJ

953

Wickens A. (2009). Introduction to Biopsychology. Pearson Education // https://books.google.ru/books?id=DlBHRK6NGsoC

954

Niedermeyer E., Silva F. d. (2005). Electroencephalography: Basic Principles, Clinical Applications, and Related Fields. Lippincott Williams & Wilkins // https://books.google.ru/books?id=tndqYGPHQdEC

955

Данилевский В. Я. (1949). Электрические явления в головном мозге / Первые отечественные исследования по электроэнцефалографии. — М. С. 87—88.

956

История изучения биоэлектрической активности головного мозга (2020) / Центр Медицинской Информации. Научная библиотека лаборатории электрофизиологии НЦИЛС // https://cmi.to/электрофизиология-2/электрофизиология-головного-мозга/история-изучения-биоэлектрической-а/

957

Шойфет М. С. (2011). Сто великих врачей. Вече // https://books.google.ru/books?id=-f2xkgEACAAJ

958

Караченцев Ю. И. (2009). К 90-летию со дня основания Института проблем эндокринной патологии им. В.Я. Данилевского: страницы истории, достижения и перспективы / Новости медицины и фармации. №10 (284) // http://www.mif-ua.com/archive/issue-9091/article-9106/

959

Введенский Н.Е. (1884). Телефонические исследования над электрическими явлениями в мышечных и нервных аппаратах. — СПб. // http://books.e-heritage.ru/book/10070047

960

Niedermeyer E., Silva F. d. (2005). Electroencephalography: Basic Principles, Clinical Applications, and Related Fields. Lippincott Williams & Wilkins // https://books.google.ru/books?id=tndqYGPHQdEC

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Pravdich-Neminsky, VV. (1913). Ein Versuch der Registrierung der elektrischen Gehirnerscheinungen / Zentralblatt für Physiologie, Vol. 27, pp. 951—60 // https://www.biodiversitylibrary.org/item/50775#page/967/mode/1up

962

^* В те годы неврология и психиатрия составляли одну специальность — нейропсихиатрию, чистая неврология в немецкоязычных странах только начинала становиться отдельной дисциплиной.

963

Niedermeyer E., Silva F. d. (2005). Electroencephalography: Basic Principles, Clinical Applications, and Related Fields. Lippincott Williams & Wilkins // https://books.google.ru/books?id=tndqYGPHQdEC

964

Ibañez D. (2014). Hans Berger: Lights and Shadows of the Inventor of Electroencephalography / NE neuroelectrics // https://www.neuroelectrics.com/blog/2014/12/18/hans-berger-lights-and-shadows-of-the-inventor-of-electroencephalography/

965

Rooney A. (2017). The History of Neuroscience. The Rosen Publishing Group, Inc // https://books.google.ru/books?id=Hd1hDwAAQBAJ

966

Radin D. (2009). Entangled Minds: Extrasensory Experiences in a Quantum Reality. New York: Paraview Pocket Books // https://books.google.ru/books?id=sUM1Hc-KwJQC

967

Wiedemann H. R. (1994). Hans Berger / European Journal of Pediatrics, Vol. 153, Iss. 10, p. 705 // https://doi.org/10.1007/BF01954482

968

Finger S. (2004). Minds behind the Brain: A History of the Pioneers and Their Discoveries. Oxford University Press // https://books.google.ru/books?id=3OWU1wnOy84C

969

Finger S. (2004). Minds behind the Brain: A History of the Pioneers and Their Discoveries. Oxford University Press // https://books.google.ru/books?id=3OWU1wnOy84C

970

Berger H. (1929). Über das Elektrenkephalogramm des Menschen / Archiv Für Psychiatrie Und Nervenkrankheiten, Bd. 87, Ausg. 1, S. 527—570 // https://doi.org/10.1007/bf01797193 // http://www.audiomentaltraining.com/app/wp-content/uploads/Berger-1929-FirstEEG.pdf

971

^* Бергер отверг неудачный, по его мнению, термин «электроцереброграмма» из-за сочетания в нём греческого и латинских корней, предложив вместо него более логичный вариант «электроэнкефалограмма» (Elektrenkephalogram), в общем-то, фонетически более правильный, чем термин, принятый в итоге научным сообществом.

972

Niedermeyer E., Silva F. d. (2005). Electroencephalography: Basic Principles, Clinical Applications, and Related Fields. Lippincott Williams & Wilkins // https://books.google.ru/books?id=tndqYGPHQdEC

973

Grass A. M. (1984). The Electroencephalographic Heritage Until 1960 / American Journal of EEG Technology, Vol. 24, pp. 133–173 // https://doi.org/10.1080/00029238.1984.11080140

974

^* Сегодня их часто называют волнами или ритмом Бергера, хотя сам учёный из скромности возражал против этого названия.

975

Niedermeyer E., Silva F. d. (2005). Electroencephalography: Basic Principles, Clinical Applications, and Related Fields. Lippincott Williams & Wilkins // https://books.google.ru/books?id=tndqYGPHQdEC

976

Berger H. (1929). Über das Elektrenkephalogramm des Menschen. Archiv Für Psychiatrie Und Nervenkrankheiten, Bd. 87, Ausg. 1, S. 527—570 // https://doi.org/10.1007/bf01797193

977

^** Пароксизмальный разряд — группа колебаний, резко отличных по структуре и амплитуде от фоновой активности; пароксизмальный разряд внезапно появляется, продолжается от долей секунды до нескольких секунд, а затем так же внезапно прекращается.

978

Niedermeyer E., Silva F. d. (2005). Electroencephalography: Basic Principles, Clinical Applications, and Related Fields. Lippincott Williams & Wilkins // https://books.google.ru/books?id=tndqYGPHQdEC

979

Zeidman L. A., Stone J., Kondziella D. (2013). New revelations about Hans Berger, father of the EEG, and his ties to the Third Reich // https://doi.org/10.1177/0883073813486558

980

Zeidman L. A., Kondziella D., Stone J. L. (2016). Authors’ Response to Letter to the Editor / Journal of Child Neurology, Vol 31, Iss. 14, 2016 // https://doi.org/10.1177/0883073816662419

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Berger H. (1940). Psyche. Jena: Gustav Fischer // https://books.google.ru/books?id=mbOgvQEACAAJ

982

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Tasaki I. (2012). Physiology and Electrochemistry of Nerve Fibers. Elsevier // https://books.google.ru/books?id=3ttzcDBIwRIC

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Fick A. (1863). Beiträge zur vergleichenden Physiologie der irritabelen Substanzen: Mit in den Text eingedruckten Holzstichen. Vieweg // https://books.google.ru/books?id=zCFCAAAAcAAJ

985

von Kries J. (1882). Ueber die Erregung des motorischen Nerven durch Wechselströme / Berichte über die Verhandlungen der Naturforschenden Gesellschaft zu Freiburg im Breisgau, Vol. 8, Iss. 2, pp. 170—204 // https://www.biodiversitylibrary.org/item/42625#page/198/mode/1up

986

Pflüger E. (1859). Untersuchungen über die Physiologie des Electrotonus. August Hirschwald.

987

Tasaki I. (2012). Physiology and Electrochemistry of Nerve Fibers. Elsevier // https://books.google.ru/books?id=3ttzcDBIwRIC

988

^* Элемент Лекланше — марганцево-цинковый элемент питания (источник тока), катод которого изготовлен из смеси графита с диоксидом марганца (MnO₂), анод — из металлического цинка, а в роли электролита выступает раствор хлорида аммония NH₄Cl.

989

Горбунов Б. Б., Востриков В. А., Нестеренко И. В., Телышев Д. В. (2018). История открытия закона Гоорвега-Вейса-Лапика / Медицинская техника. № 5 (311) // http://www.defibrillation.ru/download/Medicinskaya_texnika,2018,5,48-50.pdf

990

Hoorweg J. L. (1892). Ueber die elektrische Nervenerregung / Archiv für die gesame Physiologie des Menschen und der Tiere, Vol. 52, Iss. 3—4, pp. 87—108 // https://doi.org/10.1007/BF01661875

991

Tasaki I. (2012). Physiology and Electrochemistry of Nerve Fibers. Elsevier // https://books.google.ru/books?id=3ttzcDBIwRIC

992

Pflüger E. (1893). J. L. Hoorweg und die electrische Nervenerregung / Archiv für die gesame Physiologie des Menschen und der Tiere, Vol. 53, Iss. 11—12, p. 616

993

Weiss G. (1901). Sur la possibilité de rendre comparables entre eux les appareils servant à l’excitation électricque / Archives Italiennes de Biologie, Vol. 35, Iss. 1, pp. 413—446 // http://www.architalbiol.org/aib/article/view/35413

994

van Dongen M., Serdijn W. (2016). Design of Efficient and Safe Neural Stimulators: A Multidisciplinary Approach. Analog Circuits and Signal Processing. Springer International Publishing // https://books.google.ru/books?id=UGahCwAAQBAJ

995

Tasaki I. (2012). Physiology and Electrochemistry of Nerve Fibers. Elsevier // https://books.google.ru/books?id=3ttzcDBIwRIC

996

Lapicque L. (1909). Définition expérimentale de l'excitabilité / Comptes rendus des séances de la Société de biologie, 67, 280—283 // https://gallica.bnf.fr/ark:/12148/bpt6k6541404v/f288.image

997

Brunel N., van Rossum M. C. W. (2007). Lapicque’s 1907 paper: from frogs to integrate-and-fire / Biological Cybernetics, Vol. 97, pp. 337—339 // https://doi.org/10.1007/s00422-007-0190-0

998

Monnier A. M. (2008). Lapicque, Louis / Complete Dictionary of Scientific Biography // https://www.encyclopedia.com/science/dictionaries-thesauruses-pictures-and-press-releases/lapicque-louis

999

M. Max Lebaudy's yacht: A Mother's Neat Little Scheme Fails Of Its Aim, but Benefits Science / Los Angeles Herald, Volume 41, Number 25, 5 November 1893 // https://cdnc.ucr.edu/?a=d&d=LAH18931105&e=-------en--20--1--txt-txIN--------1

1000

Monnier A. M. (2008). Lapicque, Louis / Complete Dictionary of Scientific Biography // https://www.encyclopedia.com/science/dictionaries-thesauruses-pictures-and-press-releases/lapicque-louis

1001

Pinault M. (2000). Frédéric Joliot-Curie. O. Jacob // https://books.google.ru/books?id=ZQF1O1DLvHsC

1002

Duclert V. (1998). La Ligue de “l’epoque heroique”: la politique des savants / Le Mouvement Social, Vol. 183 (27) // https://doi:10.2307/3779613

1003

Monnier A. M. (2008). Lapicque, Louis / Complete Dictionary of Scientific Biography // https://www.encyclopedia.com/science/dictionaries-thesauruses-pictures-and-press-releases/lapicque-louis

1004

Lapicque L. (1907). Recherches quantitatives sur l’excitation electrique des nerfs traitee comme une polarization / Journal of Physiol Pathol Générale, 9, 620-635 // https://fr.wikisource.org/wiki/Recherches_quantitatives_sur_l%27excitation_%C3%A9lectrique_des_nerfs_trait%C3%A9e_comme_une_polarisation

1005

Lapicque L. (2007). Quantitative investigations of electrical nerve excitation treated as polarization. Translated by: Nicolas Brunel, Mark C. W. van Rossum / Biological Cybernetics, 2007 // https://core.ac.uk/download/pdf/21172797.pdf

1006

Горбунов Б. Б., Востриков В. А., Нестеренко И. В., Телышев Д. В. (2018). История открытия закона Гоорвега-Вейса-Лапика / Медицинская техника, октябрь // https://www.researchgate.net/publication/328579029_The_History_of_the_Discovery_of_the_Hoorweg-Weiss-Lapicque_Law

1007

Brunel N., van Rossum M. C. W. (2007). Lapicque’s 1907 paper: from frogs to integrate-and-fire / Biological Cybernetics, Vol. 97, pp. 337—339 // https://doi.org/10.1007/s00422-007-0190-0

1008

^* Потенциалом действия называют волну возбуждения, перемещающуюся по мембране живой клетки в виде кратковременного изменения мембранного потенциала (т. е. разницы в электрическом потенциале между зарядами внутренней и внешней стороны мембраны) на небольшом участке нейрона или кардиомиоцита. Далее по тексту книги мы часто для простоты будем использовать термин «импульс», хотя среди нейрофизиологов принято использовать более строгий термин «потенциал действия».

1009

^** Нейромедиаторами называют биологически активные химические вещества, посредством которых осуществляется передача электрохимического импульса через синаптическое пространство между нейронами.

1010

Monnier A. M. (2008). Lapicque, Louis / Complete Dictionary of Scientific Biography // https://www.encyclopedia.com/science/dictionaries-thesauruses-pictures-and-press-releases/lapicque-louis

1011

Tasaki I. (2012). Physiology and Electrochemistry of Nerve Fibers. Elsevier // https://books.google.ru/books?id=3ttzcDBIwRIC

1012

Monnier A. M. (2008). Lapicque, Louis / Complete Dictionary of Scientific Biography // https://www.encyclopedia.com/science/dictionaries-thesauruses-pictures-and-press-releases/lapicque-louis

1013

Davis H. (1923). The relationship of the “Chronaxie” of muscle to the size of the stimulating electrode / Journal of Physiology, Vol. 57, pp. 81—82.

1014

Rushton W. A. H. (1935). The time factor in electrical excitation / Biological Reviews, Vol. 10, Iss. 1, pp. 1—17 // https://doi.org/10.1111/j.1469-185X.1935.tb00474.x

1015

Tasaki I. (2012). Physiology and Electrochemistry of Nerve Fibers. Elsevier // https://books.google.ru/books?id=3ttzcDBIwRIC

1016

Monnier A. M. (2008). Lapicque, Louis / Complete Dictionary of Scientific Biography // https://www.encyclopedia.com/science/dictionaries-thesauruses-pictures-and-press-releases/lapicque-louis

1017

Lapicque L., Gaultier P. (1943). La machine nerveuse. (Flammarion) réédition numérique FeniXX // https://books.google.ru/books?id=r2qJDwAAQBAJ

1018

Mazliak L., Shafer G. (2011). What Does the Arrest and Release of Emile Borel and His Colleagues in 1941 Tell Us about the German Occupation of France? / Science in Context, Vol. 24, Iss. 4, pp. 587—623, December 2011 // https://doi.org/10.1017/S0269889711000238

1019

Peltier C. Louis Édouard Lapicque (1866–1952) // http://www.charleslapicque.fr/a-propos-de/biographie/biographie-detaillee/resources/pdf/Louis_Lapicque.pdf

1020

Lykknes A., Opitz D. L., Van Tiggelen B. (2012). For Better or For Worse? Collaborative Couples in the Sciences. Science Networks. Historical Studies. Springer Basel // https://books.google.ru/books?id=yR0fPFFbKqsC

1021

Abbott L. F. (1997). Lapicque’s introduction of the integrate-and-fire model neuron / Brain Research Bulletin, Vol. 50, Iss. 5—6, November—December 1999, pp. 303—304 // https://doi.org/10.1016/S0361-9230(99)00161-6

1022

Liang P., Wu S., Gu F. (2015). An Introduction to Neural Information Processing. Springer Netherlands // https://books.google.ru/books?id=XFZECwAAQBAJ

1023

Calvo P., Gomila T. (2008). Handbook of Cognitive Science: An Embodied Approach. Elsevier Science // https://books.google.ru/books?id=jxnhqHuo3gQC

1024

Brunel N., van Rossum M. C. W. (2007). Lapicque’s 1907 paper: from frogs to integrate-and-fire / Biological Cybernetics, Vol. 97, pp. 337—339 // https://doi.org/10.1007/s00422-007-0190-0

1025

Adrian E. D. (1932). Nobel Lecture, December 12, 1932 // https://www.nobelprize.org/prizes/medicine/1932/adrian/lecture/

1026

Finger S. (2004). Minds behind the Brain: A History of the Pioneers and Their Discoveries. Oxford University Press // https://books.google.ru/books?id=3OWU1wnOy84C

1027

Bowditch H. P (1871). Über die Eigenthümlichkeiten der Reizbarkeit, welche die Muskelfasern des Herzens zeigen / Arbeiten aus der Physiologischen Anstalt zu Leipzig // https://echo.mpiwg-berlin.mpg.de/ECHOdocuView?url=/permanent/vlp/lit1387/index.meta

1028

Rosenblueth A. (1935). The All-or-None Principle and the Nerve Effector Systems / The Quarterly Review of Biology, Vol. 10, No. 3, pp. 334-340 // https://doi.org/10.1086/394489

1029

Lucas K. (1905). On the gradation of activity in a skeletal muscle-fibre / The Journal of Physiology, Vol. 33, Iss. 2, pp. 125—137 // https://doi.org/10.1113/jphysiol.1905.sp001115

1030

Lucas K. (1909). The "all-or-none" contraction of the amphibian skeletal muscle fibre / The Journal of Physiology, Vol. 38, Iss. 2—3, pp. 113-133 // https://doi.org/10.1113/jphysiol.1909.sp001298

1031

Smith D. L. (1963). Basic Concepts in Physiology: II. Keith Lucas and the Nerve-Muscle Response / The American Biology Teacher, Vol. 25, Iss. 8, pp. 610—615 // https://doi.org/10.2307/4440465

1032

Finger S. (2004). Minds behind the Brain: A History of the Pioneers and Their Discoveries. Oxford University Press // https://books.google.ru/books?id=3OWU1wnOy84C

1033

Piccolino M. (2003). Nerves, alcohol and drugs, the Adrian–Kato controversy on nervous conduction: deep insights from a “wrong” experiment? / Brain Research Reviews, Vol. 43, Iss. 3, pp. 257—265 // https://doi.prg/10.1016/j.brainresrev.2003.08.006

1034

Adrian E. D. (1932). Nobel Lecture, December 12, 1932 // https://www.nobelprize.org/prizes/medicine/1932/adrian/lecture/

1035

Finger S. (2004). Minds behind the Brain: A History of the Pioneers and Their Discoveries. Oxford University Press // https://books.google.ru/books?id=3OWU1wnOy84C

1036

Piccolino M., Bresadola M. (2013). Shocking Frogs: Galvani, Volta, and the Electric Origins of Neuroscience. Oxford University Press // https://books.google.ru/books?id=_VYGAQAAQBAJ

1037

Lucas K., Adrian E. D. (1917). The Conduction of the Nervous Impulse. Longmans, Green and Company // https://books.google.ru/books?id=fNVOAAAAMAAJ

1038

Cowan W. M., Südhof T. C., Stevens C. P. (2003). Synapses. JHU Press // https://books.google.ru/books?id=FO5efrKGVQoC

1039

Finger S. (2004). Minds behind the Brain: A History of the Pioneers and Their Discoveries. Oxford University Press // https://books.google.ru/books?id=3OWU1wnOy84C

1040

Gasser H. S., Newcomer H. S. (1921). Physiological action currents in the phrenic nerve. An application of the thermionic vacuum tube to nerve physiology / The American Journal of Physiology, Vol. 57, Iss. 1, pp. 1—26 // https://doi.org/10.1152/ajplegacy.1921.57.1.1

1041

Павлов А. 5 июля 1888 г / Critical: Сайт медицины критических состояний. Календарь // https://www.critical.ru/calendar/0507gasser.htm

1042

Gasser H. S., Erlanger J. (1929). Role of size in establishment of nerve block by pressure or cocaine / The American Journal of Physiology, Vol. 88, pp. 581—589.

1043

1044

Kato G.-I. (1970). The road a scientist followed. Notes of Japanese Physiology as I myself experienced it / Annual Review of Physiology, 1970, Vol. 32, pp. 1—22 // https://doi.org/10.1146/annurev.ph.32.030170.000245

1045

Adrian E. D. (1932). Nobel Lecture, December 12, 1932 // https://www.nobelprize.org/prizes/medicine/1932/adrian/lecture/

1046

The Nobel Prize in Physiology or Medicine 1944. NobelPrize.org. Nobel Media AB 2020, 30 Oct 2020 // https://www.nobelprize.org/prizes/medicine/1944/summary/

1047

^* Частотно-импульсная модуляция — такой вид импульсной модуляции, при которой управление средним значением выходного параметра осуществляется за счёт изменения частоты следования импульсов, обладающих неизменной длительностью.

1048

1049

Finger S. (2004). Minds behind the Brain: A History of the Pioneers and Their Discoveries. Oxford University Press // https://books.google.ru/books?id=3OWU1wnOy84C

1050

Сандаков Д. Б. (2011). Возбуждение и его механизмы / Электронный учебник по курсу «Физиология человека и животных» // http://www.bio.bsu.by/phha/01/01_text.html

1051

Сазонов В. Ф. (2011). Функциональная классификация мембранных ионных каналов / Научные труды III Съезда физиологов СНГ. — М.: Медицина-Здоровье. С. 72 // http://www.physiology-cis.org/files/YA2011_Proceedings.pdf

1052

Сазонов В. Ф. (2017). Ионные каналы мембраны / Кинезиолог // http://kineziolog.bodhy.ru/content/ionnye-kanaly-membrany

1053

Zangari A., Micheli D., Galeazzi R., Tozzi A. (2018). Node of Ranvier as an Array of Bio-Nanoantennas for Infrared Communication in Nerve Tissue / Scientific Reports, Vol. 8, p. 539 // https://doi.org/10.1038/s41598-017-18866-x

1054

Castelfranco A. M., Hartline D. K. (2015). The evolution of vertebrate and invertebrate myelin: a theoretical computational study / Journal of Computational Neuroscience, Vol. 38, pp. 521—538 // https://doi.org/10.1007/s10827-015-0552-x

1055

Hodgkin A. L., Huxley A. F. (1952). A quantitative description of membrane current and its application to conduction and excitation in nerve / The Journal of Physiology. 117 (4): 500–44 // https://doi.org/10.1113%2Fjphysiol.1952.sp004764

1056

Forrest M. D. (2014). Can the Thermodynamic Hodgkin–Huxley Model of Voltage-Dependent Conductance Extrapolate for Temperature? / Computation, Vol. 2, Iss. 2, pp. 47—60 // https://doi.org/10.3390%2Fcomputation2020047

1057

Pakdaman K., Thieullen M., Wainrib G. (2010). Fluid limit theorems for stochastic hybrid systems with applications to neuron models / Advances in Applied Probability, Vol. 42, Iss. 3, pp. 761—794 // https://doi.org/10.1239/aap/1282924062

1058

Zheng Q., Wei G. W. (2011). Poisson-Boltzmann-Nernst-Planck model / Journal of Chemical Physics, 134 (19): 194101 // https://doi.org/10.1063%2F1.3581031

1059

Tai-Chia Lin T.-C. (2011). The Poisson The Poisson-Nernst-Planck (PNP) system for ion transport (PNP) system for ion transport / 3rd OCAMI-TIMS Workshop in Japan, Osaka, March 13—16, 2011 // http://www.sci.osaka-cu.ac.jp/~ohnita/2010/TCLin.pdf

1060

Nagumo J., Arimoto S., Yoshizawa S. (1962). An active pulse transmission line simulating nerve axon / Proceedings of the IRE, Vol. 50, pp. 2061—2070 // https://ieeexplore.ieee.org/document/4066548

1061

Izhikevich E. M. (2003). Simple model of spiking neurons / IEEE transactions on neural networks, Vol. 14, No. 6, November 2003 // http://www.rctn.org/vs265/izhikevich-nn03.pdf

1062

MacGregor R. (2012). Neural and Brain Modeling. Elsevier // https://books.google.ru/books?id=0vOiz7Ztx10C

1063

Briggman K. L., Helmstaedter M., Denk W. (2011). Wiring specificity in the direction-selectivity circuit of the retina / Nature, vol. 471, Iss. 7337, pp. 183—188 // https://doi.org/10.1038/nature09818

1064

Kim J. S., Greene M. J., Zlateski A., Lee K., Richardson M., Turaga S. C., Purcaro M., Balkam M., Robinson A., Behabadi B. F., Campos M., Denk W., Seung H. S. (2014). Space–time wiring specificity supports direction selectivity in the retina / Nature, Vol. 509, Iss. 7500, pp. 331—336 // https://doi.org/10.1038%2Fnature13240

1065

^* Биполярные клетки (bipolar cells) обычно имеют веретенообразную форму и два отростка (один аксон и один дендрит), именно поэтому их и называют биполярными. В сетчатке они соединяют через синапсы одну колбочку или несколько палочек зрительной системы с одной ганглионарной или амакриновой клеткой (последнее характерно для биполярных клеток палочек).

1066

^** Амакриновые клетки (amacrine cells) получили название от греческой приставки α (не-) и слов μακρός (длинный) и ίνα (волокно). Амакриновые клетки — это тормозящие нейроны, выходы которых соединяются с ганглионарными клетками сетчатки и/или с биполярными клетками.

1067

^*** Ганглионарные клетки (retinal ganglion cells, RGC) — слой нейронов, расположенных в непосредственной близости от внутренней поверхности сетчатки. Они генерируют сигналы, которые затем передаются в зрительную кору.

1068

Коровски Ю. (2015). Игры ради науки / XX2 век, 23 марта // https://22century.ru/popular-science-publications/games-for-science

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Zlateski A., Lee K., Seung H. S. (2017). Scalable training of 3D convolutional networks on multi-and many-cores / Journal of Parallel and Distributed Computing, Vol. 106, pp. 195—204 // https://doi.org/10.1016/j.jpdc.2017.02.006

1070

^* Нейронаука — междисциплинарная область знаний, занимающаяся изучением нейронных процессов.

1071

Alivisatos P. A., Chun M., Church G. M., Greenspan R. J., Roukes M. L., Yuste R. (2012). The Brain Activity Map Project and the Challenge of Functional Connectomics / Neuron, Vol. 74, Iss. 6, pp. 970—974, June 21, 2012 // https://doi.org/10.1016/j.neuron.2012.06.006

1072

Jürgens K. D. (2002). Etruscan shrew muscle: the consequences of being small. The Journal of Experimental Biology. 205 (Pt 15): 2161–2166 // https://www.ncbi.nlm.nih.gov/pubmed/12110649

1073

1074

White J. G., Southgate E., Thomson J. N., Brenner S. (1986). The structure of the nervous system of the nematode Caenorhabditis elegans / Philosophical Transactions of the Royal Society B, Vol. 314, Iss. 1165, 12 November 1986, pp. 1—340 // https://doi.org/10.1098/rstb.1986.0056

1075

Ryan R., Lu Z., Meinertzhagen I. A. (2016). The CNS connectome of a tadpole larva of Ciona intestinalis (L.) highlights sidedness in the brain of a chordate sibling / eLife 2016; 5:e16962 // https://doi.org/10.7554/eLife.16962

1076

DeWeerdt S. (2019). How to map the brain / Nature, Vol. 571, S6-S8, 24 July 2019 // https://www.nature.com/articles/d41586-019-02208-0

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Cook S. J., Jarrell T. A., Brittin C. A., Wang Y., Bloniarz A. E., Yakovlev M. A., Nguyen K. C. Q., Tang L. T.-H., Bayer E. A., Duerr J. S., Bülow H. E., Hobert O., Hall D. H., Emmons S. W. (2019). Whole-animal connectomes of both Caenorhabditis elegans sexes / Nature, Vol. 571, pp. 63—71 // https://doi.org/10.1038/s41586-019-1352-7

1078

Zheng Z., Lauritzen J. S., Perlman E., Robinson C. G., Nichols M., Milkie D., Torrens O., Price J., Fisher C. B., Sharifi N., Calle-Schuler S. A., Kmecova L., Ali I. J., Karsh B., Trautman E. T., Bogovic J. A., Hanslovsky P., Jefferis G. S. X. E., Kazhdan M., Khairy K., Saalfeld S., Fetter R. D., Bock D. D. A Complete Electron Microscopy Volume of the Brain of Adult Drosophila melanogaster / Cell, Vol. 174, Iss. 3, pp. 730—743.E22, July 26, 2018 // https://doi.org/10.1016/j.cell.2018.06.019

1079

Li P. H., Maitin-Shepard J. (2019). An Interactive, Automated 3D Reconstruction of a Fly Brain / Google AI Blog, August 5, 2019 // https://ai.googleblog.com/2019/08/an-interactive-automated-3d.html

1080

Li P. H., Lindsey L. F., Januszewski M., Zheng Z., Bates A. S., Taisz I., Tyka M., Nichols M., Li F., Perlman E., Maitin-Shepard J., Blakely T., Leavitt L., Jefferis G. S. X. E., Bock D., Jain V. (2019). Automated Reconstruction of a Serial-Section EM Drosophila Brain with Flood-Filling Networks and Local Realignment // https://doi.org/10.1101/605634

1081

Jain V., Januszewski M. (2018). Improving Connectomics by an Order of Magnitude / Google AI Blog, July 16, 2018 // https://ai.googleblog.com/2018/07/improving-connectomics-by-order-of.html

1082

Blakely T. (2021). A Browsable Petascale Reconstruction of the Human Cortex / Google AI Blog, June 1, 2021 // https://ai.googleblog.com/2021/06/a-browsable-petascale-reconstruction-of.html

1083

Shapson-Coe A., Januszewski M., Berger D. R., Pope A., Wu Y., Blakely T., Schalek R. L., Li P., Wang S., Maitin-Shepard J., Karlupia N., Dorkenwald S., Sjostedt E., Leavitt L., Lee D., Bailey L., Fitzmaurice A., Kar R., Field B., Wu H., Wagner-Carena J., Aley D., Lau J., Lin Z., Wei D., Pfister H., Peleg A., Jain V., Lichtman J. W. (2021). A connectomic study of a petascale fragment of human cerebral cortex // https://doi.org/10.1101/2021.05.29.446289

1084

Explore H01: One cubic millimeter of the human cerebral cortex (2021) // https://h01-release.storage.googleapis.com/explore.html

1085

Wilhelm B. G., Mandad S., Truckenbrodt S., Kröhnert K., Schäfer C., Rammner B., Koo S. J., Claßen G. A., Krauss M., Haucke V., Urlaub H., Rizzoli S. O. (2014). Composition of isolated synaptic boutons reveals the amounts of vesicle trafficking proteins. / Science, Vol. 344, Iss. 6187, pp. 1023—1028 // https://doi.org/10.1126/science.1252884

1086

Doerr A. (2014). Modeling the synapse / Nature Methods, Vol. 11, pp. 788–789 // https://doi.org/10.1038/nmeth.3057

1087

^* Астроцит (от греч. άστρον — звезда и κύτος — клетка) — тип нейроглиальной клетки звёздчатой формы с многочисленными отростками.

1088

Jolivet R., Coggan J. S., Allaman I., Magistretti P. J. (2015). Multi-timescale Modeling of Activity-Dependent Metabolic Coupling in the Neuron-Glia-Vasculature Ensemble / PLOS Computational Biology, February 26, 2015. // https://doi.org/10.1371/journal.pcbi.1004036

1089

de Ceglia R., Ledonne A., Litvin D. G., Lind B. L., Carriero G., Latagliata E. C., Bindocci E., Di Castro M. A., Savtchouk I., Vitali I., Ranjak A., Congiu M., Canonica T., Wisden W., Harris K., Mameli M., Mercuri N., Telley L., Volterra A. (2023). Specialized astrocytes mediate glutamatergic gliotransmission in the CNS. / Nature, Vol. 262, 06 September 2023. // https://doi.org/10.1038/s41586-023-06502-w

1090

DeWeerdt S. (2019). How to map the brain / Nature, Vol. 571, S6-S8, 24 July 2019 // https://www.nature.com/articles/d41586-019-02208-0

1091

OpenWorm foundation (2022). OpenWorm // https://openworm.org/

1092

Haspel G., Boyden E. S., Brown J., Church G., Cohen N., Fang-Yen C., Flavell S., Goodman M. B., Hart A. C., Hobert O., Kagias K., Lockery S., Lu Y., Marblestone A., Matelsky J., Pfister H., Rotstein H. G., Scholz M., Shlizerman E., Simeon Q., Skuhersky M. A., Venkatachalam V., Yang G. R., Yemini E., Zimmer M., Kording K. P. (2023). To reverse engineer an entire nervous system // https://arxiv.org/abs/2308.06578

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Сегеда Г. (2022). Цифровой двойник головастика — ещё один шаг на пути к искусственному разуму? / Наука в Сибири, 31 янв. // https://sbras.info/articles/nauka-dlya-obschestva/cifrovoy-dvoynik-golovastika-esche-odin-shag-na-puti-k

1094

Ferrario A., Palyanov A., Koutsikou S., Li W., Soffe S., Roberts A., Borisyuk R. (2021). From decision to action: Detailed modelling of frog tadpoles reveals neuronal mechanisms of decision-making and reproduces unpredictable swimming movements in response to sensory signals / PLOS Computational Biology, December 13, 2021 // https://doi.org/10.1371/journal.pcbi.1009654

1095

McCulloch W. S., Pitts W. (1943). A logical calculus of the ideas immanent in nervous activity / Bulletin of Mathematical Biophysics, 5: 115 // https://doi.org/10.1007/BF02478259

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Heims S. J. (1991). Describing “Embodiments of mind”: McCulloch and his cohorts / Chrisley R., Begeer S. (2000). Artificial Intelligence: Critical Concepts. Routledge // https://books.google.ru/books?id=dLQ3bDy2tgYC

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Conway F., Siegelman J. (2009). Dark Hero of the Information Age: In Search of Norbert Wiener, The Father of Cybernetics. Basic Books // https://books.google.ru/books?id=u_w4DgAAQBAJ

1098

Gefter A. (2015). The Man Who Tried to Redeem the World with Logic: Walter Pitts rose from the streets to MIT, but couldn’t escape himself / Nautilus, February 5, 2015 // https://nautil.us/the-man-who-tried-to-redeem-the-world-with-logic-235253/

1099

^* Венский кружок (нем. Wiener Kreis) — группа учёных, регулярно собиравшаяся в Вене в конце 20-х — середине 30-х гг. XX в. С деятельностью Венского кружка обычно связывают появление логического позитивизма.

1100

Arbib M. A. (2016). Foreword to the 2016 reussue / McCulloch W. S., Papert S. (2016). Embodiments of Mind. MIT Press // https://books.google.ru/books?id=ITxMDQAAQBAJ

1101

Conway F., Siegelman J. (2009). Dark Hero of the Information Age: In Search of Norbert Wiener, The Father of Cybernetics. Basic Books // https://books.google.ru/books?id=u_w4DgAAQBAJ

1102

Abraham T. H. (2002). (Physio)logical circuits: The intellectual origins of the McCulloch-Pitts neural networks / Journal of the History of the Behavioral Sciences, Vol. 38, Iss. 1, pp. 3—25 // https://doi.org/10.1002/jhbs.1094

1103

Shmailov M. M. (2012). Intellectual Pursuits of Nicolas Rashevsky. The Queer Duck of Biology // https://books.google.ru/books?id=usHsDAAAQBAJ

1104

Орехова К. В. (2010). Дворянский Род Ржевских в Дзержинске / Городской журнал Светский в Дзержинске // http://www.svetsky.com/dvoryanskoe-gnezdo-dzerzhinska/dvorianskii-rod-rzhevskikh-v-dzerzhinske

1105

Модзалевский В. Л. (2012). Малороссийский родословник. Т. 4. С. 220—432 // https://books.google.ru/books?id=OuaWBgAAQBAJ

1106

Shmailov M. M. (2012). Intellectual Pursuits of Nicolas Rashevsky. The Queer Duck of Biology // https://books.google.ru/books?id=usHsDAAAQBAJ

1107

Рашевский Н. П. (1920). Н. П. Рашевский — В. И. Вернадскому. № 706, 23 октября 1920 / Вернадський В. I. (2012). Вибрані наукові праці академіка В.І. Вернадського. Т. 2: Володимир Іванович Вернадський. Листування з українськими вченими.

1108

Невзорова И. М. (2007). Таврида в изгнании / «Серебряный век» в Крыму: взгляд из XXI столетия. Материалы Четвёртых Герцыковских чтений в г. Судаке 6—10 июня 2005 года.

1109

Shmailov M. M. (2012). Intellectual Pursuits of Nicolas Rashevsky. The Queer Duck of Biology // https://books.google.ru/books?id=usHsDAAAQBAJ

1110

Harman O., Dietrich M. R. (2012). Outsider Scientists: Routes to Innovation in Biology. University of Chicago Press // https://books.google.ru/books?id=yffPAQAAQBAJ

1111

Nicolas Rashevsky / Worddisk // https://www.worddisk.com/wiki/Nicholas_Rashevsky/

1112

Shmailov M. M. (2012). Intellectual Pursuits of Nicolas Rashevsky. The Queer Duck of Biology // https://books.google.ru/books?id=usHsDAAAQBAJ

1113

Shmailov M. M. (2012). Intellectual Pursuits of Nicolas Rashevsky. The Queer Duck of Biology // https://books.google.ru/books?id=usHsDAAAQBAJ

1114

Rashevsky N. P. (1924). Is Time the Fourth Dimension? / Scientific American, Vol. 131, Iss. 6 / https://www.scientificamerican.com/article/is-time-the-fourth-dimension/

1115

Current Opinion, Vol. 78, 1924, p. 78.

1116

^* Дисперсными называют системы, состоящие как минимум из двух фаз, одна из которых мелко раздроблена и равномерно распределена во второй, сплошной фазе. В зависимости от размера частиц дисперсной фазы выделяют грубодисперсные (с размером частиц больше 100 нм) и тонкодисперсные (с размером частиц от 1 до 100 нм), или коллоидные, системы. Если же размер частиц дисперсной фазы становится меньше 1 нм, то система становится раствором.

1117

Shmailov M. M. (2012). Intellectual Pursuits of Nicolas Rashevsky. The Queer Duck of Biology // https://books.google.ru/books?id=usHsDAAAQBAJ

1118

Rosen R. (1991). Life Itself: A Comprehensive Inquiry Into the Nature, Origin, and Fabrication of Life. Columbia University Press // https://books.google.ru/books?id=DR8L4snDnkIC

1119

Shmailov M. M. (2012). Intellectual Pursuits of Nicolas Rashevsky. The Queer Duck of Biology // https://books.google.ru/books?id=usHsDAAAQBAJ

1120

^* Один из вариантов этого анекдота: «Собрали биолога, математика и физика и попросили их придумать что-нибудь, чтобы всегда выигрывать на бегах. Через год учёные рассказывают о своих достижениях.

Биолог: Зная точную родословную лошади, успехи её родителей, чем её кормили, как лечили, я могу точно назвать максимальную скорость.

Математик: Имея точные статистические данные предыдущих забегов этих лошадей, я могу назвать приблизительные результаты этого.

Физик: Мне нужно ещё десять лет, пятьдесят миллионов долларов, несколько помощников и лаборатория, но я уже построил модель движения сферического коня в вакууме».

1121

Anderson J., Rosenfeld E. (2000). Talking Nets: An Oral History of Neural Networks. New York, NY, USA: MIT Press // https://books.google.ru/books?id=-l-yim2lNRUC

1122

Shmailov M. M. (2012). Intellectual Pursuits of Nicolas Rashevsky. The Queer Duck of Biology // https://books.google.ru/books?id=usHsDAAAQBAJ

1123

Abraham T. H. (2004). Nicolas Rashevsky’s Mathematical Biophysics / Journal of the History of Biology, Vol. 37, Iss. 2, pp. 333—385 / https://doi.org/10.1023/b:hist.0000038267.09413.0d

1124

Conway F., Siegelman J. (2009). Dark Hero of the Information Age: In Search of Norbert Wiener, The Father of Cybernetics. Basic Books // https://books.google.ru/books?id=u_w4DgAAQBAJ

1125

Gefter A. (2015). The Man Who Tried to Redeem the World with Logic: Walter Pitts rose from the streets to MIT, but couldn’t escape himself / Nautilus, February 5, 2015 // http://nautil.us/issue/21/information/the-man-who-tried-to-redeem-the-world-with-logic

1126

Day Staff Writer (2009). Old Lyme family looks to future of land with guidance from past / The Day, published March 13.2000, updated December 29, 2009 // https://www.theday.com/article/20000313/DAYARC/303139970

1127

Malapi-Nelson A. (2017). The Nature of the Machine and the Collapse of Cybernetics: A Transhumanist Lesson for Emerging Technologies. Palgrave Studies in the Future of Humanity and its Successors. Springer International Publishing // https://books.google.ru/books?id=-g0rDwAAQBAJ

1128

Levine Y. (2019). Surveillance Valley: The Secret Military History of the Internet. Icon Books Limited // https://books.google.ru/books?id=Rph5DwAAQBAJ

1129

Levine Y. (2019). Surveillance Valley: The Secret Military History of the Internet. Icon Books Limited // https://books.google.ru/books?id=Rph5DwAAQBAJ

1130

Joby Milo A. (1994). In Eves' circles. MAA notes 34. Mathematical Association of America // https://books.google.ru/books?id=CNzuAAAAMAAJ

1131

Chang S. (2011). Academic Genealogy of Mathematicians. World Scientific // https://books.google.ru/books?id=4siw31DPONUC

1132

Powell A. B., Frankenstein M. (2000). Remembering Dirk Jan Struik, 1894-2000 // https://www.maa.org/news/remembering-dirk-jan-struik-1894-2000

1133

Chang S. (2013). The Secret Guide to Computers. Springer Science & Business Media // https://books.google.ru/books?id=gMYGCAAAQBAJ

1134

Hardesty L. (2011). The Original Absent-Minded Professor / MIT Technology Review, Jun 21, 2011 // https://www.technologyreview.com/s/424363/the-original-absent-minded-professor/

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Priestley M. (2011). A Science of Operations: Machines, Logic and the Invention of Programming. Springer London // https://books.google.ru/books?id=uflV0_q-FEUC

1137

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Rosenbluelh А., Wiener N., Bigelow J. (1943). Behavior, Purpose and Teleology / Philosophy of Science, 1943, Vol. 10, No. 1, pp. 18—24 // https://doi.org/10.1086/286788

1139

Masani P. R. (1990). Norbert Wiener 1894–1964. Vita Mathematica. Birkhäuser // https://books.google.ru/books?id=TpT_GfMId-sC

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Kline R. R. (2015). The Cybernetics Moment: Or Why We Call Our Age the Information Age. New Studies in American Intellectual and Cultural History. JHU Press // https://books.google.ru/books?id=NQPHCQAAQBAJ

1142

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1143

Шутина Ю. (2017). Год разоблачения сенсаций. Главные открытия и достижения археологов в 2016 г. / Meduza, 5 янв. // https://meduza.io/feature/2017/01/05/god-razoblacheniya-sensatsiy

1144

von Neumann J. (1945). First Draft of a Report on the EDVAC. Moore School of Electrical Engineering. University of Pennsylvania / IEEE Annals of the History of Computing, vol. 15, No. 1, 1993 // http://web.mit.edu/STS.035/www/PDFs/edvac.pdf

1145

1146

Moye W. T. (1996). ENIAC: The Army-Sponsored Revolution. United States Army Research Laboratory // http://ftp.arl.army.mil/mike/comphist/96summary/index.html

1147

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1149

Kline R. (2015). The Cybernetics Moment: Or Why We Call Our Age the Information Age. Johns Hopkins University Press // https://books.google.ru/books?id=WgPHCQAAQBAJ

1150

Soni J., Goodman R. (2017). A Mind at Play: How Claude Shannon Invented the Information Age. Simon & Schuster // https://books.google.ru/books?id=ABlpDQAAQBAJ

1151

Smalheiser N. (2000). Walter Pitts / Perspectives in biology and medicine, 43, pp. 217—226 // https://doi.org/10.1353/pbm.2000.0009

1152

^* Пенеплен (в геоморфологии) — практически ровная, местами слабовсхолмлённая поверхность, которая была сформирована на месте древних гор.

1153

^** Аноэтический — не полностью сознающий; находящийся на грани сознания.

1154

^*** Номотет — законодатель; у афинян: член совета, назначенный для испытания перемен, предполагавшихся в законах Солона.

1155

1156

1157

1158

Franchi S., Bianchini F. (2011). The Search for a Theory of Cognition: Early Mechanisms and New Ideas. Rodopi // https://books.google.ru/books?id=aRzSx0Jse-0C

1159

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1161

Kleene S. (1951). Representation of events in nerve nets and finite automata // https://www.rand.org/content/dam/rand/pubs/research_memoranda/2008/RM704.pdf

1162

^* Это буква «тета», а не ноль, перерубленный пополам; я мог бы заменить её на другую букву без перемены смысла, но всё-таки решил оставить её ради аутентичности, а также для того, чтобы читателям, боящимся математических выражений, в этом месте было страшнее.

1163

McCulloch W. S., Pitts W. (1943). A logical calculus of the ideas immanent in nervous activity / Bulletin of Mathematical Biophysics, 5: 115 // https://doi.org/10.1007/BF02478259

1164

von Neumann J. (1951). The General and Logical Theory of Automata / Jeffress L. A. (1951). Cerebral Mechanisms in Behavior: The Hixon Symposium. Wiley. New York // https://books.google.ru/books?id=0vgMAAAAIAAJ

1165

Rosenblatt F. (1961). Principles of Neurodynamics. Perceptrons and the Theory of Brain Mechanisms. Cornell aeronautical lab inc., Buffalo, New York. Defense Technical Information Center // https://books.google.ru/books?id=Tk2tDAEACAAJ

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Piccinini G. (2004). The First Computational Theory of Mind and Brain: A Close Look at Mcculloch and Pitts's “Logical Calculus of Ideas Immanent in Nervous Activity” / Synthese, Vol. 141 (2) // https://doi.org/10.1023/B:SYNT.0000043018.52445.3e

1167

Kleene S. (1951). Representation of events in nerve nets and finite automata // https://www.rand.org/content/dam/rand/pubs/research_memoranda/2008/RM704.pdf

1168

Pierpoint N. (2009). Why are regular expressions called “regular” expressions? / StackOverflow, Jun 10 '09 // https://stackoverflow.com/questions/975465/why-are-regular-expressions-called-regular-expressions

1169

Wright P. (2012). Why is a regular language called 'regular'? / StackExchange, May 10 '12 // https://cs.stackexchange.com/questions/1771/why-is-a-regular-language-called-regular/1772

1170

Weller T. (2016). How did Regex get its name? / StackExchange, Mar 9 '16 // https://ell.stackexchange.com/questions/83917/how-did-regex-get-its-name

1171

1172

Kleene S. (1951). Representation of events in nerve nets and finite automata // https://www.rand.org/content/dam/rand/pubs/research_memoranda/2008/RM704.pdf

1173

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Landahl H. D., McCulloch W. S., Pitts W. (1943). A statistical consequence of the logical calculus of nervous nets. The Bulletin of Mathematical Biophysics, 5(4), 135–137 // https://doi.org/10.1007/bf02478260

1175

Turing A. (1946). Turing Letter to W. Ross Ashby // http://www.rossashby.info/letters/turing.html

1176

Copeland B. J. (2012). Alan Turing's Electronic Brain: The Struggle to Build the ACE, the World's Fastest Computer. OUP Oxford // https://books.google.ru/books?id=YhQZnczOS7kC

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Turing A. (1948). Intelligent Machinery // http://www.alanturing.net/intelligent_machinery/

1178

Gabbay D., Woods J., Thagard P. (2006). Philosophy of Psychology and Cognitive Science. Handbook of the Philosophy of Science. Elsevier Science // https://books.google.ru/books?id=Lp93PtrvM0MC

1179

Turing A. (1948). Intelligent Machinery // http://www.alanturing.net/intelligent_machinery/

1180

Shimbel A., Rapoport A. (1948). A statistical approach to the theory of the central nervous system. The Bulletin of Mathematical Biophysics, 10(1), 41–55 // https://doi.org/10.1007/bf02478329

1181

Hebb D. (1949). The Organization of Behavior: A Neuropsychological Theory. A Wiley book in clinical psychology. Wiley // https://books.google.ru/books?id=dZ0eDiLTwuEC

1182

Thorndike E. L., Bruce D. (1970). Animal Intelligence: Experimental Studies. Transaction Publishers // https://books.google.ru/books?id=Go8XozILUJYC

1183

Thorndike E. L. (1932). The Fundamentals Of Learning. Teachers College, Columbia University // https://archive.org/details/in.ernet.dli.2015.157080/page/n29

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1185

Майоров Ф. П. (1948). История учения об условных рефлексах. — М.: Академия Медицинских наук СССР // http://anfiz.ru/books/item/f00/s00/z0000021/index.shtml

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1187

^* Гиропилот (также гирорулевой) — электронавигационный прибор, работающий на основании показаний гирокомпаса. Гиропилот осуществляет автоматическое удержание судна на заданном курсе с гораздо большей точностью, чем это может делать человек, использующий компас.

1188

Hoggett R. (2009). 1951 — SNARC Maze Solver — Minsky / Edmonds (American) / cyberneticzoo.com: a history of cybernetic animals and early robots // http://cyberneticzoo.com/mazesolvers/1951-maze-solver-minsky-edmonds-american/

1189

Bernstein J. (1981). A.I / The New Yorker, December 6, 1981 // https://www.newyorker.com/magazine/1981/12/14/a-i

1190

Klein D. (2018). Mighty mouse / MIT Technology Review, December 19, 2018 // https://www.technologyreview.com/2018/12/19/138508/mighty-mouse/

1191

Cannon W. B. (1932). The Wisdom of the Body, Vol. 10. W. W. Norton, Incorporated // https://books.google.ru/books?id=zdkEAQAAIAAJ

1192

Pfeiffer J. E. (1949). The Stuff That Dreams Are Made On; CYBERNETICS: Or Control and Communication in the Animal and the Machine. By Norbert Wiener. 191 pp. New York: John Wiley & Sons / The New York Times, Jan. 23, 1949 // https://www.nytimes.com/1949/01/23/archives/the-stuff-that-dreams-are-made-on-cybernetics-or-control-and.html

1193

Science: The Thinking Machine (1949) / Time, Monday, Jan. 24, 1949 // http://content.time.com/time/subscriber/article/0,33009,799721,00.html

1194

Ashby W. R. (1960). Design for a Brain. The origin of adaptive behaviour. Second edition. Springer Netherlands // https://books.google.ru/books?id=QsIXAAAAMAAJ

1195

Ashby W. R. (1949). The Electronic Brain / Radio-Electronics, Mar. 1949 // http://www.rossashby.info/gallery/Radio%20Electronics%20March%201949%20The%20Electronic%20Brain.pdf

1196

Ashby W. R. (1948). Design for a Brain / Electronic Engineering, Vol. 20, pp. 379—383.

1197

Pickering A. (2009). Psychiatry, synthetic brains and cybernetics in the work of W. Ross Ashby / International Journal of General Systems, Vol. 38, Iss. 2, pp. 213—230 // https://doi.org/10.1080/03081070802712025

1198

Rid T. (2016). Rise of the Machines: A Cybernetic History. W. W. Norton & Company // https://books.google.ru/books?id=WByZCgAAQBAJ

1199

Рид Т. (2020). Рождение машин. Неизвестная история кибернетики / Пер. с англ. Е. Васильченко, Е. Кузьмина. Litres // https://books.google.ru/books?id=0CCNDwAAQBAJ

1200

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1201

Pickering A. (2002). Cybernetics And The Mangle: Ashby, Beer And Pask / Social Studies of Science, Vol. 32, Iss. 3 // https://doi.org/10.1177/0306312702032003003

1202

Pilcher H. (1948). 390625 thoughts. The clicking brain is clever than man's / Daily Herald, No. 10227, Dec. 13, 1948 // https://www.britishnewspaperarchive.co.uk/viewer/BL/0000681/19481213/001/0001

1203

Pias C., Foerster G. v. (2016). Cybernetics: The Macy Conferences 1946-1953: The Complete Transactions. The University of Chicago Press // https://books.google.ru/books?id=zOincQAACAAJ

1204

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1205

Boden M. A. (2006). Grey Walter’s Anticipatory Tortoises / The Rutherford Journal, Vol. 2, 2006-2007 // http://www.rutherfordjournal.org/article020101.html

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Marsh A. (2020). Meet the Roomba’s Ancestor: The Cybernetic Tortoise / IEEE Spectrum, 28 Feb 2020 // https://spectrum.ieee.org/tech-history/space-age/meet-roombas-ancestor-cybernetic-tortoise

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Марш А. (2020). Познакомьтесь с кибернетической черепахой, предшественником Roomba / Пер. с англ. Голованов А. / Хабр, 24 марта 2020 // https://habr.com/ru/post/493482/

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Pickering A. (2010). The cybernetic brain. Sketches of another future. The University of Chicago Press // http://www.arise.mae.usp.br/wp-content/uploads/2018/03/Andrew-Pickering-Cybernetic-Brain_Cap.3.pdf

1209

^* Паттерн (от англ. pattern — узор, шаблон, образец, схема) здесь часто означает образ, шаблон, повторяющийся элемент.

1210

Gabbay D., Woods J., Thagard P. (2006). Philosophy of Psychology and Cognitive Science. Elsevier Science // https://books.google.ru/books?id=Lp93PtrvM0MC

1211

Farley B., Clark W. (1954). Simulation of self-organizing systems by digital computer / Transactions of the IRE Professional Group on Information Theory, Vol. 4 (4), pp. 76—84 // https://doi.org/10.1109/tit.1954.1057468

1212

Clark W., Farley B. (1954). Generalization of pattern recognition in a self-organizing system / Proceedings of the March 1-3, 1955, western joint computer conference, pp. 86—91 //https://doi.org/10.1145/1455292.1455309

1213

Rochester N., Holland J., Haibt L., Duda W. (1956). Tests on a cell assembly theory of the action of the brain, using a large digital computer. IEEE Transactions on Information Theory, 2(3), 80–93 // https://doi.org/10.1109/tit.1956.1056810

1214

Gabbay D., Woods J., Thagard P. (2006). Philosophy of Psychology and Cognitive Science. Elsevier Science // https://books.google.ru/books?id=Lp93PtrvM0MC

1215

Davis B. (2012). New Rochelle. Arcadia Publishing // https://books.google.ru/books?id=v5o78L0q_wQC

1216

Kennedy K. (2016). Lasting Impact: One Team, One Season. What Happens When Our Sons Play Football. Time Incorporated Books // https://books.google.ru/books?id=qMi_DAAAQBAJ

1217

YIVO Institute of Jewish Research (2013). Frank Rosenblatt / Guide to the YIVO archives // http://www.yivoarchives.org/index.php?p=collections/controlcard&id=33295

1218

Goldsmith S. A. (1927). Dr. Frank F. Rosenblatt / The Jewish Social Service Quarterly. Stanford. The Berman Jewish Policy Archive // https://www.jewishdatabank.org/search-results/publication/12586

1219

Coblentz S., Elliot J., Burgess S. (1993). Adventures of a Freelancer: The Literary Exploits and Autobiography of Stanton A. Coblentz. Borgo Press // https://books.google.ru/books?id=Bd9R-hcy7iEC

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Бейзер М. (2014). Трудности «дистанционного управления» в истории «Джойнта» на примере его работы в России — СССР / Труды по еврейской истории и культуре. Материалы XXI ежегодной конференции по иудаике, вып. 50 // https://sefer.ru/upload/Conf-21.text.1-575(25.12.14).pdf

1221

Scates S. (2006). Maurice Rosenblatt and the Fall of Joseph McCarthy. University of Washington Press // https://books.google.ru/books?id=8y53AAAAMAAJ

1222

Schudel M. (2005). Lobbyist Maurice Rosenblatt Dies / The Washington Post, August 15, 2005 // https://www.washingtonpost.com/archive/local/2005/08/15/lobbyist-maurice-rosenblatt-dies/572aad97-92b3-42fa-9e32-c0636e12be99/

1223

Dorrien G. (2018). Breaking White Supremacy: Martin Luther King Jr. and the Black Social Gospel. Yale University Press // https://books.google.ru/books?id=rjlFDwAAQBAJ

1224

Sejnowski T. (2018). The Deep Learning Revolution. New York, NY, USA: MIT Press // https://books.google.ru/books?id=9xZxDwAAQBAJ

1225

Emlen S. T., Howland H. C., O’Brien R. D. (1971). Frank Rosenblatt, July 11, 1928 — July 11, 1971: Cornell University Faculty Memorial Statement // https://ecommons.cornell.edu/bitstream/handle/1813/18965/Rosenblatt_Frank_1971.pdf

1226

Rosenblatt F. (1957). The Perceptron: A Perceiving and Recognizing Automaton. Project Para Report No. 85-460-1, Cornell Aeronautical Laboratory // https://blogs.umass.edu/brain-wars/files/2016/03/rosenblatt-1957.pdf

1227

Rosenblatt F. (1957). The perceptron: A Probabilistic model for Visual Perception / Proceedings of the 15th International Congress of Psychology, North Holland, pp. 290—297

1228

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LeCun Y., Cortes C., Burges C. J. C. (1998). The MNIST database of handwritten digits // http://yann.lecun.com/exdb/mnist/

1231

Kussul E., Baidyk T., Kasatkina L., Lukovich V. (2001). Rosenblatt perceptrons for handwritten digit recognition / IJCNN’01. International Joint Conference on Neural Networks. Proceedings (Cat. No.01CH37222) // https://doi.org/10.1109/ijcnn.2001.939589

1232

Backus J. (1978). Can Programming Be Liberated from the Von Neumann Style? A Functional Style and Its Algebra of Programs / Communications of the ACM, 21(8), pp. 613—641 // http://doi.acm.org/10.1145/359576.359579

1233

Anderson J., Rosenfeld E. (2000). Talking Nets: An Oral History of Neural Networks. New York, NY, USA: MIT Press // https://books.google.ru/books?id=-l-yim2lNRUC

1234

Douglas S. C. (1995). Generalized gradient adaptive step sizes for stochastic gradient adaptive filters / 1995 International Conference on Acoustics, Speech, and Signal Processing, Vol. 2, Iss. 8, pp. 1396—1399 // https://doi.org/10.1109/ICASSP.1995.480502

1235

Anderson J., Rosenfeld E. (2000). Talking Nets: An Oral History of Neural Networks. New York, NY, USA: MIT Press // https://books.google.ru/books?id=-l-yim2lNRUC

1236

Hilberg v. W. (1995). Karl Steinbuch, ein zu Unrecht vergessener Pionier der künstlichen neuronalen Systeme / Frequenz, Vol. 49, pp. 1—2 // https://www.degruyter.com/downloadpdf/j/freq.1995.49.1-2/freq.1995.49.1-2.28/freq.1995.49.1-2.28.pdf

1237

Karl Steinbuch: von der Kybernetik zur Politik (2017) / Heinz Nixdorf MuseumsForum Blog, 15.06.2017 // https://blog.hnf.de/karl-steinbuch-von-der-kybernetik-zur-politik/

1238

Bishop C. M. (2006). Pattern Recognition and Machine Learning. Information science and statistics. Springer New York // https://books.google.ru/books?id=kOXDtAEACAAJ

1239

United States. Office of Naval Research (1960). Research device recognizes objects or patterns / Naval Research Reviews, Volume 13, 4-Feb-1960 // https://books.google.ru/books?id=ve9cbfGDaywC

1240

^* Brain (англ.) — мозг.

1241

^** В греческой мифологии Минос был сыном Зевса и Европы и властителем Крита, а после смерти стал одним из трёх судей в подземном мире.

1242

Nilsson N. J. (2009). The Quest for Artificial Intelligence. Cambridge University Press // https://books.google.ru/books?id=nUJdAAAAQBAJ

1243

Huber W. A. (1968). Graphical data processing / Pattern Identification by Man and Machine. Proceedings of a Planning Conference Held at Texas Christian University, Fort Worth, Texas, 12-13 December, 1968 // https://books.google.ru/books?id=vaQlAAAAMAAJ

1244

Duda R. O., Nilsson N. J. (1965). Calculus of networks of adaptive elements. Proposal for Research SRI No. ESU 65-12R. Proposal Request 1-6-4400 // https://www.sri.com/wp-content/uploads/pdf/1284.pdf

1245

Nilsson N. J. (2009). The Quest for Artificial Intelligence. Cambridge University Press // https://books.google.ru/books?id=nUJdAAAAQBAJ

1246

^* Shake (англ.) — дрожь.

1247

Fan S. (2019). Will AI Replace Us: A Primer for the 21st Century. Volume 0 of The Big Idea Series. Thames & Hudson // https://books.google.ru/books?id=5iapDwAAQBAJ

1248

Keay A., Silicon Valley Robotics (2017). Shakey is first robot to receive IEEE Milestone award / Robohub, February 28, 2017 // https://robohub.org/shakey-is-first-robot-to-receive-ieee-milestone-award/

1249

Mao L. (2017). Comprehensive Proof of Perceptron Convergence Theorem // https://leimao.github.io/blog/Perceptron-Convergence-Theorem/

1250

Витушкин А. Г. (2004). 13-я проблема Гильберта и смежные вопросы / Успехи математических наук. Т. 59, вып. 1 (355). С. 11—24 // https://doi.org/10.4213/rm698

1251

Tavora M. (2018). Connections between Neural Networks and Pure Mathematics / freeCodeCamp, 12 December 2018 // https://www.freecodecamp.org/news/connections-between-deep-learning-physics-and-pure-mathematics-part-i-947abeb3a5dd/

1252

Hu J. (2015). Between Us: A Queer Theorist’s Devoted Husband and Enduring Legacy / The New Yorker, December 9, 2015 // https://www.newyorker.com/books/page-turner/between-us-a-queer-theorists-devoted-husband-and-enduring-legacy

1253

Sedgwick H. A. (2017). Life of Eve Kosofsky Sedgwick / A resource for the exploration of the life and work of Eve Kosofsky Sedgwick // https://eveksedgwickfoundation.org/biography/biography.html

1254

Sedgwick H. A. (2016). The Cornell Student Homophile League // http://www.jearldmoldenhauer.com/wp-content/uploads/Cornell-Final5X.pdf

1255

^* Интрацистернально — в подпаутинное пространство головного мозга.

1256

Røigaard-Petersen, H. H., Nissen, T., Fjerdingstad, E. J. (1968). Effect of ribonucleic acid (RNA) extracted from the brain of trained animals on learning in rats / Scandinavian Journal of Psychology, Vol. 9, Iss. 1, pp. 1–16 // https://doi.org/10.1111/j.1467-9450.1968.tb00512.x

1257

Ungar G., Oceguera-Navarro C. (1965). Transfer of Habituation by Material extracted from Brain / Nature, vol. 207, 1965, pp. 301—302 // https://doi.org/10.1038/207301a0

1258

Setlow B. (1997). Georges Ungar and memory transfer / Journal of the history of the neurosciences, 6, pp. 181—192 // https://doi.org/10.1080/09647049709525701

1259

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^* Значение метода наименьших квадратов, разработанного в начале XVIII в. Гауссом и Лежандром, для машинного обучения столь значительно, что один из отцов современных нейронных сетей Юрген Шмидхубер даже называет модели Гаусса и Лежандра «линейными нейронными сетями» или «линейными перцептронами».

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^* Один из вариантов этого анекдота: «Некий студент решил поставить опыт. Поймал таракана, положил на стол и начал стучать по столу. Таракан убежал. Затем студент начал отрывать по одной лапке у таракана и обнаружил, что с каждым разом таракан реагирует на стук всё хуже. Потом, когда все лапки были оторваны, студент постучал по столу, но таракан никуда не убежал. В итоге студент сделал вывод, что таракан оглох».

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Grill J.-B., Strub F., Altché F., Tallec C., Richemond P. H., Buchatskaya E., Doersch C., Pires B. A., Guo Z. D., Azar M. G., Piot B., Kavukcuoglu K., Munos R., Valko M. (2020). Bootstrap your own latent: A new approach to self-supervised Learning // https://arxiv.org/abs/2006.07733

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^* Иногда в популярных источниках называют срок, равный 18 месяцам, — он связан с прогнозом Давида Хауса, многолетнего главы компании Intel, который считал, что производительность процессоров должна удваиваться каждые 18 месяцев за счёт комбинации действия закона Мура и увеличения тактовых частот процессоров. Ретроспективная оценка показывает, что прогноз Хауса был близок к истине, более поздние оценки дают величину, равную примерно 20 месяцам.

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^* Её также называют уравнением Ферхюльста. Пьер Ферхюльст — бельгийский математик, занимавшийся среди прочего моделированием изменения численности населения, рост которого ограничен имеющимися в распоряжении популяции ресурсами, позже эту же кривую неоднократно переоткрывали и применяли для описания динамики различных процессов, например автокаталитических реакций, роста опухолей, изменения лексики в естественных языках и, наконец, распространения инноваций.

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^* Фотоникой (от греческого φῶς, φωτὁς — свет) называют дисциплину, занимающуюся различными аспектами работы с оптическими сигналами, а также созданием разных устройств на их основе; нанофотоника — это раздел фотоники, изучающий физические процессы, возникающие при взаимодействии фотонов с объектами нанометрового масштаба.

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^* Дит — единица количества информации, содержащейся в сообщении о данном состоянии системы, имеющей десять равновероятных состояний.

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^* Словом «толóка» в России в прошлом называли форму деревенской взаимопомощи, толоку организовывали для выполнения срочных работ, требующих объединения усилий большого количества работников: сооружения дома или постройки дороги, вырубки леса и так далее.

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^* Социальная сеть для поиска и установления деловых контактов, запрещённая в Российской Федерации.

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Число вакансий в сфере искусственного интеллекта в РФ выросло за год в 2,5 раза (2018) / Прайм: агентство экономической информации, 10 Ноября 2018 // https://1prime.ru/telecommunications_and_technologies/20181110/829424812.html

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Over 2M students have enrolled in Machine Learning MOOC from Stanford (2019) / MoocLab // https://www.mooclab.club/threads/over-2m-students-have-enrolled-in-machine-learning-mooc-from-stanford.11562/

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Введение в машинное обучение / Coursera // https://ru.coursera.org/learn/vvedenie-mashinnoe-obuchenie

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Stanford Human-Centered Artificial Intelligence (HAI) (2019). Artificial Intelligence Index Report 2019 // https://hai.stanford.edu/sites/default/files/ai_index_2019_report.pdf

1783

^* Фискальный, или финансовый, год (fiscal year) федерального правительства США длится с 1 октября предыдущего года по 30 сентября текущего.

1784

The Networking & Information Technology R&D Program and the National Artificial Intelligence Initiative Office (2022). Supplement to the President’s FY2023 budget // https://www.nitrd.gov/pubs/FY2023-NITRD-NAIIO-Supplement.pdf

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Congressional Research Service (2021). Artificial Intelligence: Background, Selected Issues, and Policy Considerations // https://crsreports.congress.gov/product/pdf/R/R46795

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Acharya A., Arnold Z. (2019). Chinese Public AI R&D Spending: Provisional Findings. CSET Issue Brief // https://cset.georgetown.edu/wp-content/uploads/Chinese-Public-AI-RD-Spending-Provisional-Findings-1.pdf

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Pawlyk O. (2018). China Leaving US Behind on Artificial Intelligence: Air Force General / Military.com // https://www.military.com/defensetech/2018/07/30/china-leaving-us-behind-artificial-intelligence-air-force-general.html

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Hao K. (2019). Yes, China is probably outspending the US in AI—but not on defense / MIT Technology Review, Dec 5, 2019 // https://www.technologyreview.com/s/614842/china-us-ai-military-spending/

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State Council Notice on the Issuance of the New Generation Artificial Intelligence Development Plan (2017) // https://www.newamerica.org/cybersecurity-initiative/digichina/blog/full-translation-chinas-new-generation-artificial-intelligence-development-plan-2017/

1792

新一代人工智能发展规划 (2017) // http://www.gov.cn/zhengce/content/2017-07/20/content_5211996.htm

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Паспорт федерального проекта «Цифровые технологии», с. 22, сумма за 2020-2014 годы // https://digital.gov.ru/uploaded/files/pasport-federalnogo-proekta-tsifrovyie-tehnologii.pdf

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International Federation of Robotics (IFR) (2021). Press Conference World Robotics 2021 // https://ifr.org/downloads/press2018/2021_10_28_WR_PK_Presentation_long_version.pdf

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Гапотченко Д. (2021). Выручка участников рейтинга увеличилась в 1,5 раза, несмотря на эпидемию и локдаун / С-News, 27 сентября 2021 // https://www.cnews.ru/reviews/promyshlennie_roboty_2021/articles/vyruchka_uchastnikov_rejtinga_uvelichilas

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International Federation of Robotics (IFR) (2022). Executive Summary World Robotics 2022 Industrial Robots // https://ifr.org/img/worldrobotics/Executive_Summary_WR_Industrial_Robots_2022.pdf

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International Federation of Robotics (IFR) (2023). China overtakes USA in robot density // https://ifr.org/ifr-press-releases/news/china-overtakes-usa-in-robot-density

1801

Гапотченко Д. (2021). Промышленные роботы пострадали от ковида, но меньше, чем ожидалось / С-News, 27 сентября 2021 // https://www.cnews.ru/reviews/promyshlennie_roboty_2021/articles/promyshlennye_roboty_postradali_ot

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АО АК «ДЕЛОВОЙ ПРОФИЛЬ» (2021). Использование промышленных роботов: обзор рынка робототехники в России и мире // https://delprof.ru/press-center/open-analytics/ispolzovanie-promyshlennykh-robotov-obzor-rynka-robototekhniki-v-rossii-i-mire/

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Скрынникова А. (2019). Больше всего роботов в России покупает автопром / Ведомости, 19 сентября 2019 // https://www.vedomosti.ru/technology/articles/2019/09/19/811579-bolshe-vsego-robot

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Sizing the prize. PwC’s Global Artificial Intelligence Study: Exploiting the AI Revolution. What’s the real value of AI for your business and how can you capitalise? (2017) // https://www.pwc.com/gx/en/issues/data-and-analytics/publications/artificial-intelligence-study.html

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Gregory R. (1929). Discovery: Or, The Spirit and Service of Science. Macmillan // https://books.google.ru/books?id=IwVJygEACAAJ

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Shapiro F. R. (2008). Our Daily Bleg: Did I. B. M. Really See a World Market “For About Five Computers”? // https://freakonomics.com/2008/04/17/our-daily-bleg-did-ibm-really-see-a-world-market-for-about-five-computers/

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Deloitte.Insights (2019). Future in the balance? How countries are pursuing an AI advantage. Insights from Deloitte’s State of AI in the Enterprise, 2nd Edition survey // https://www2.deloitte.com/content/dam/Deloitte/lu/Documents/public-sector/lu-global-ai-survey.pdf

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Deloitte.Insights (2019). Tech Trends 2019: Beyond the digital frontier // https://www2.deloitte.com/content/dam/Deloitte/br/Documents/technology/DI_TechTrends2019.pdf

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Bughin J., Seong J., Manyika J., Chui M., Joshi R. (2018). Notes from the ai frontier modeling the impact of ai on the world economy. Discussion paper / McKinsey&Company. McKinsey Global Institute // https://www.mckinsey.com/~/media/McKinsey/Featured%20Insights/Artificial%20Intelligence/Notes%20from%20the%20frontier%20Modeling%20the%20impact%20of%20AI%20on%20the%20world%20economy/MGI-Notes-from-the-AI-frontier-Modeling-the-impact-of-AI-on-the-world-economy-September-2018.ashx

1815

^* Экстерналия (англ. externality), или внешний эффект, в экономической теории — воздействие рыночной транзакции на третьих лиц, не опосредованное рынком. Например, загрязнение окружающей среды в результате деятельности некой компании является отрицательной экстерналией.

1816

ITUTrends (2018). Assessing the Economic Impact of Artificial Intelligence / Emerging trends in ICTs, Iss. Paper No. 1, September 2018 // https://www.itu.int/dms_pub/itu-s/opb/gen/S-GEN-ISSUEPAPER-2018-1-PDF-E.pdf

1817

^* Вообще говоря, термин модальность (от лат. modus — способ) пришёл в информатику из психологии, в которой понятия «модальность раздражителя» [stimulus modality] и «сенсорная модальность» [sensory modality] используются для того, чтобы указать на восприятие раздражителя определённой сенсорной системой: визуальной (зрительной), аудиальной (слуховой) и так далее. Однако использование этого термина в области информатики приобрело весьма вольный характер. Например, нередко говорят о «текстовой модальности» [text modality], но ведь у человека отсутствуют специальные сенсоры для восприятия текста — мы воспринимаем текст опосредованно, например через зрительную или слуховую систему. Фактически в данном случае термин «модальность» смешивается со способом представления данных [data representation]. Кроме того, очевидно, что машины вовсе не обязаны иметь тот же набор сенсорных систем, что и люди. Увы, связанная с этим путаница в наши дни приобрела уже всеобщий масштаб, и фарш уже вряд ли получится прокрутить в обратном направлении. Но, быть может, ещё не поздно при необходимости использовать для различения смешавшихся понятий составные термины, например «сенсорная модальность» и «модальность представления» [representation modality].

1818

Portes Q., Carvalho J. M., Pinquier J., Lerasle F. (2021). Multimodal Neural Network for Sentiment Analysis in Embedded Systems // https://www.scitepress.org/Papers/2021/102247/102247.pdf

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Baltrušaitis T., Ahuja C., Morency L.-P. (2018). Multimodal Machine Learning: A Survey and Taxonomy // https://arxiv.org/abs/1705.09406

1820

From not working to neural networking: The artificial-intelligence boom is based on an old idea, but with a modern twist (2016) / The Economist // https://www.economist.com/special-report/2016/06/23/from-not-working-to-neural-networking

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Sánchez J., Perronnin F., Mensink T. (2010). Improved Fisher Vector for Large Scale Image Classification XRCE's participation for ILSVRC // http://image-net.org/challenges/LSVRC/2010/ILSVRC2010_XRCE.pdf

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Lin Y., Lv F., Zhu S., Yang M., Cour T., Yu K., Cao L., Li Z., Tsai M., Zhou X., Huang T., Zhang T. (2010). ImageNet classification: fast descriptor coding and large-scale SVM training // http://image-net.org/challenges/LSVRC/2010/ILSVRC2010_NEC-UIUC.pdf

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Perronnin F., Sánchez J. (2011). XRCE@ILSVRC2011: Compressed Fisher vectors for LSVR // http://image-net.org/challenges/LSVRC/2011/ilsvrc11.pdf

1824

Jessi H. (2018). Fei-Fei Li's Quest To Make Ai Better For Humanity / Wired, 11.13.2018 // https://www.wired.com/story/fei-fei-li-artificial-intelligence-humanity/

1825

^* На самом деле в официальном архиве датасета, выложенном на сайте Caltech, наличествуют 102 папки вместо 101. По всей видимости, «безбилетником» стала папка BACKGROUND_Google, содержащая довольно странный набор изображений, начиная от карты путешествий генерала Ферье по Персии и Афганистану размером 3481 × 2955 пикселей и заканчивая красноречивой карикатурой, на которой изображён человек со спущенными штанами, демонстрирующий зрителям свой голый зад; сей шедевр сопровождается подписью «C:\». Вероятно, в набор просто попала папка с персональной свалкой картинок кого-то из создателей датасета. Желаю удачи цифровым археологам будущего в её исследовании.

1826

Fei-Fei L., Fergus R., Perona P. The Caltech 101 // http://www.vision.caltech.edu/Image_Datasets/Caltech101/

1827

Griffin G., Holub A. D., Perona P. The Caltech 256 // http://www.vision.caltech.edu/Image_Datasets/Caltech256/

1828

Ponce J., Berg T. L., Everingham M., Forsyth D. A., Hebert M., Lazebnik S., Marszalek M., Schmid C., Russell B. C., Torralba A., Williams C. K. I., Zhang J., Zisserman A. (2006). Dataset Issues in Object Recognition / Ponce J., Hebert M., Schmid C., Zisserman A. (2006). Toward Category-Level Object Recognition. Lecture Notes in Computer Science, Vol. 4170. Springer, Berlin, Heidelberg // https://doi.org/10.1007/11957959_2

1829

^* Словарь, в котором указаны семантические отношения (синонимы, антонимы и т. д.) между лексическими единицами.

1830

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Braslavski P., Ustalov D., Mukhin M., Kiselev Y. (2016). YARN: Spinning-in-Progress / Proceedings of the Eight Global Wordnet Conference, — Bucharest, Romania, 2016 — pp. 58—65 // https://russianword.net/

1832

Липатов А., Гончарук А., Гельфенбейн И., Шило В., Лехельт В. Русский Wordnet // http://wordnet.ru/

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Loukachevitch N. V., Lashevich G., Gerasimova A. A., Ivanov V. V., Dobrov B. V. (2016). Creating Russian WordNet by Conversion. / Proceedings of Conference on Computational linguistics and Intellectual technologies Dialog-2016, pp. 405-415

1834

Лашевич Г. (2021). Тезаурус русского языка RuWordNet // https://www.ruwordnet.ru

1835

Zisserman A., Winn J., Fitzgibbon A., Gool L. V., Sivic J., Williams C., Hogg D. (2012). In Memoriam: Mark Everingham / IEEE Transactions on pattern analysis and machine intelligence, Vol. 34, No. 11, November 2012 // https://doi.org/10.1109/TPAMI.2012.204

1836

^* Команда SuperVision отправляла ещё одну версию сети, при обучении которой к обучающей выборке были добавлены изображения с прошлогодних соревнований, и эта модель смогла «выгадать» ещё чуть более процентного пункта, сократив ошибку до 15,32%, но поскольку некоторые исследователи считают это не совсем честным трюком, то в прессе часто приводят первое значение.

1837

Russakovsky O., Deng J., Su H., Krause J., Satheesh S., Ma S., Huang Z., Karpathy A., Khosla A., Bernstein M., Berg A. C., Fei-Fei L. (2015). ImageNet Large Scale Visual Recognition Challenge / International Journal of Computer Vision, Vol. 115, pp. 211–252 // https://doi.org/10.1007/s11263-015-0816-y

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Deng J., Berg A., Satheesh S., Su H., Khosla A., Fei-Fei L. (2012). Large Scale Visual Recognition Challenge 2012 (ILSVRC2012). Held in conjunction with PASCAL Visual Object Classes Challenge 2012 (VOC2012) // http://image-net.org/challenges/LSVRC/2012/index

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Gershgorn D. (2018). Rise of AlexNet: The inside story of how AI got good enough to dominate Silicon Valley / QUARTZ, June 18, 2018 // https://qz.com/1307091/the-inside-story-of-how-ai-got-good-enough-to-dominate-silicon-valley/

1840

Krizhevsky A. (2009). Learning Multiple Layers of Features from Tiny Images // https://www.cs.toronto.edu/~kriz/learning-features-2009-TR.pdf

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Ustik G. (2020). MIT removes huge dataset that teaches AI systems to use racist, misogynistic slurs / TheNextWeb, July 1, 2020 // https://thenextweb.com/neural/2020/07/01/mit-removes-huge-dataset-that-teaches-ai-systems-to-use-racist-misogynistic-slurs/

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Gorey C. (2020). 80m images used to train AI pulled after researchers find string of racist terms / siliconrepublic, 13 Jul 2020 // https://www.siliconrepublic.com/machines/mit-database-racist-misogynist-discovery-abeba-birhane

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Quach K. (2020). MIT apologizes, permanently pulls offline huge dataset that taught AI systems to use racist, misogynistic slurs. Top uni takes action after El Reg highlights concerns by academics / The Register, 1 Jul 2020 // https://www.theregister.com/2020/07/01/mit_dataset_removed/

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Krizhevsky A., Sutskever I., Hinton G. E. (2012). ImageNet Classification with Deep Convolutional Neural Networks / Advances in Neural Information Processing Systems 25 (NIPS 2012) // https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf

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Bai K. (2019). A Comprehensive Introduction to Different Types of Convolutions in Deep Learning: Towards intuitive understanding of convolutions through visualizations / Towards Data Science, Feb 12, 2019 // https://towardsdatascience.com/a-comprehensive-introduction-to-different-types-of-convolutions-in-deep-learning-669281e58215

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Liu D. (2017). A Practical Guide to ReLU: Start using and understanding ReLU without BS or fancy equations // https://medium.com/@danqing/a-practical-guide-to-relu-b83ca804f1f7

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Krizhevsky A., Sutskever I., Hinton G. E. (2012). ImageNet Classification with Deep Convolutional Neural Networks (Slides) // http://image-net.org/challenges/LSVRC/2012/supervision.pdf

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Godoy D. (2018). Hyper-parameters in Action! Part II — Weight Initializers / Towards Data Science, Jun 18, 2018 // https://towardsdatascience.com/hyper-parameters-in-action-part-ii-weight-initializers-35aee1a28404

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Glorot X., Bengio Y. (2010). Understanding the difficulty of training deep feedforward neural networks / Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, Journal of Machine Learning Research, Vol. 9, pp. 249—256 // http://www.jmlr.org/proceedings/papers/v9/glorot10a/glorot10a.pdf

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He K., Zhang X., Ren S., Sun J. (2015). Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification / Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV), pp. 1026—1034 // https://doi.org/10.1109/ICCV.2015.123

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Liang X. (2019). Understand Kaiming Initialization and Implementation Detail in PyTorch: Initialization Matters! Know how to set the fan_in and fan_out mode with kaiming_uniform_ function / Towards Data Science, Aug 7, 2019 // https://towardsdatascience.com/understand-kaiming-initialization-and-implementation-detail-in-pytorch-f7aa967e9138

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Zhu C., Ni R., Xu Z., Kong K., Huang W. R., Goldstein T. (2021). GradInit: Learning to Initialize Neural Networks for Stable and Efficient Training // https://arxiv.org/abs/2102.08098

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Krizhevsky A., Sutskever I., Hinton G. E. (2012). ImageNet Classification with Deep Convolutional Neural Networks (Slides) // http://image-net.org/challenges/LSVRC/2012/supervision.pdf

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Karpathy A. CS231n Convolutional Neural Networks for Visual Recognition (Stanford CS class) // http://cs231n.github.io/convolutional-networks/

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Girard R. (2015). How does Krizhevsky's '12 CNN get 253,440 neurons in the first layer? / StackExchange // https://stats.stackexchange.com/questions/132897/how-does-krizhevskys-12-cnn-get-253-440-neurons-in-the-first-layer

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Chellapilla K., Puri S., Simard P. (2006). High performance convolutional neural networks for document processing / International Workshop on Frontiers in Handwriting Recognition, 2006 // https://hal.inria.fr/inria-00112631

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Nasse F., Thurau C., Fink G. A. (2009). Face Detection Using GPU-Based Convolutional Neural Networks / International Conference on Computer Analysis of Images and Patterns, CAIP 2009 // https://doi.org/10.1007/978-3-642-03767-2_10

1865

^* Под ансамблем в машинном обучении понимают объединение нескольких моделей для решения одной задачи, позволяющее достичь лучшего результата, чем при использовании каждой модели по отдельности; для получения результирующего прогноза ансамбля результаты входящих в него моделей могут усредняться либо комбинироваться каким-то более сложным образом.

1866

Cireșan D., Meier U., Masci J., Schmidhuber J. (2012). Multi-Column Deep Neural Network for Traffic Sign Classification // http://people.idsia.ch/~juergen/nn2012traffic.pdf

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Schmidhuber J. 2011: First Superhuman Visual Pattern Recognition. IJCNN 2011 competition in Silicon Valley: twice better than humans, three times better than the closest artificial competitor, six times better than the best non-neural method // http://people.idsia.ch/~juergen/superhumanpatternrecognition.html

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Tsang S.-H. (2018). Review: ZFNet — Winner of ILSVRC 2013 (Image Classification) // https://medium.com/coinmonks/paper-review-of-zfnet-the-winner-of-ilsvlc-2013-image-classification-d1a5a0c45103

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Tsang S. H. (2018). Review: ZFNet — Winner of ILSVRC 2013 (Image Classification) // https://medium.com/coinmonks/paper-review-of-zfnet-the-winner-of-ilsvlc-2013-image-classification-d1a5a0c45103

1870

^* Во многих популярных статьях, посвящённых результатам ILSVRC-2014, результирующая ошибка указана равной 6,67%. На самом деле точное значение ошибки — 0,06656, то есть 6,66%. Интересно, кто так «округлил» результат? И сделано ли это было во славу Господа?

1871

Das S. (2017). CNN Architectures: LeNet, AlexNet, VGG, GoogLeNet, ResNet and more… // https://medium.com/analytics-vidhya/cnns-architectures-lenet-alexnet-vgg-googlenet-resnet-and-more-666091488df5

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Tsang S. H. (2018). Review: GoogLeNet (Inception v1)— Winner of ILSVRC 2014 (Image Classification) // https://medium.com/coinmonks/paper-review-of-googlenet-inception-v1-winner-of-ilsvlc-2014-image-classification-c2b3565a64e7

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Simonyan K., Zisserman A. (2015). Very deep convolutional networks for large-scale image recognition // https://arxiv.org/abs/1409.1556

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Shao J., Zhang X., Ding Z., Zhao Y., Chen Y., Zhou J., Wang W., Mei L., Hu C. (2016). Good Practices for Deep Feature Fusion // http://image-net.org/challenges/talks/2016/Trimps-Soushen@ILSVRC2016.pdf

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Hu J., Shen L, Sun G. (2018). Squeeze-and-Excitation Networks / IEEE Conference on Computer Vision and Pattern Recognition // https://github.com/hujie-frank/SENet

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Reynolds M. (2017). New computer vision challenge wants to teach robots to see in 3D / New Scientist, Iss. 3121 // https://www.newscientist.com/article/2127131-new-computer-vision-challenge-wants-to-teach-robots-to-see-in-3d/

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Medical Image Net: A petabyte-scale, cloud-based, multi-institutional, searchable, open repository of diagnostic imaging studies for developing intelligent image analysis systems // http://langlotzlab.stanford.edu/projects/medical-image-net/

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Chang S. F., Liu D., Ye G., Li Y., Xu H., Liu H., Wang D., Lin T., Chen Q., Shou Z. A Large Scale Structured Concept Library // http://eventnet.cs.columbia.edu/index.html

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Kapitanov A., Kvanchiani K., Nagaev A., Petrova E. (2023). Slovo: Russian Sign Language Dataset // https://arxiv.org/abs/2305.14527

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1894

^* Дискретное преобразование Фурье — это операция, которая позволяет разложить функцию, представленную набором её значений, взятых с некоторым шагом (в нашем случае — амплитуд звуковой волны), в виде разложения элементарных гармонических колебаний с разными частотами (подобно тому как музыкальный аккорд можно разложить на отдельные звуковые колебания, соответствующие составляющим его нотам). Быстрое преобразование Фурье — алгоритм ускоренного вычисления дискретного преобразования Фурье.

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Cieri C., Graff D., Kimball O., Miller D., Walker K. (2005). Fisher English Training Part 2, Transcripts // https://catalog.ldc.upenn.edu/LDC2005T19

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Linguistic Data Consortium (2002). 2000 HUB5 English Evaluation Transcripts LDC2002T43. Web Download. Philadelphia: Linguistic Data Consortium // https://catalog.ldc.upenn.edu/LDC2002T43

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NIST March 2000 Hub-5 Benchmark Test Results for Recognition of Conversational Speech over the Telephone, in English and Mandarin. Release 1.4 (2000) // https://catalog.ldc.upenn.edu/docs/LDC2002T43/readme.htm

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The 2000 NIST Evaluation Plan for Recognition of Conversational Speech over the Telephone. Version 1.3, 24-Jan-00 (2000) // https://mig.nist.gov/MIG_Website/tests/ctr/2000/h5_2000_v1.3.html

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Sainath T. N., Mohamed A., Kingsbury B., Ramabhadran B. (2013). Deep convolutional neural networks for LVCSR / 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, Vancouver, BC, 2013, pp. 8614-8618 // https://doi.org/10.1109/ICASSP.2013.6639347

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1933

Park D. S., Chan W., Zhang Y., Chiu C. C., Zoph B., Cubuk E. D., Le Q. V. (2019). SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition // https://arxiv.org/abs/1904.08779

1934

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1935

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1936

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1937

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1938

Zhang Y., Qin J., Park D. S., Han W., Chiu C.-C., Pang R., Le Q. V., Wu Y. (2020). Pushing the Limits of Semi-Supervised Learning for Automatic Speech Recognition // https://arxiv.org/abs/2010.10504

1939

Xu Q., Baevski A., Likhomanenko T., Tomasello P., Conneau A., Collobert R., Synnaeve G., Auli M. (2020). Self-training and Pre-training are Complementary for Speech Recognition // https://arxiv.org/abs/2010.11430

1940

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1941

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1943

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1944

Silver D., Huang A., Maddison C. J., Guez A., Sifre L., van den Driessche G., Schrittwieser J., Antonoglou I., Panneershelvam V., Lanctot M., Dieleman S., Grewe D., Nham J., Kalchbrenner N., Sutskever I., Lillicrap T., Leach M., Kavukcuoglu K., Graepel T., Hassabis D. (2016). Mastering the game of Go with deep neural networks and tree search / Nature, Vol. 529(7587), pp. 484—489 // https://doi.org/10.1038/nature16961

1945

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1946

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1947

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1948

International Joint Conferences on Artificial Intelligence Organization (2017). Deepmind Alphago Team Receives Inaugural Ijcai Marvin Minsky Medal For Outstanding Achievements in AI / Communications of the ACM, October 20, 2017 // https://cacm.acm.org/news/222067-deepmind-alphago-team-receives-inaugural-ijcai-marvin-minsky-medal-for-outstanding-achievements-in-ai/fulltext

1949

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1950

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1951

Silver D., Schrittwieser J., Simonyan K., Antonoglou I., Huang A., Guez A., Hubert T., Baker L., Lai M., Bolton A., Chen Y., Lillicrap T., Fan H., Sifre L., Driessche G., Graepel T., Hassabis D. (2017). Mastering the game of Go without human knowledge / Nature, Vol. 550 (7676), pp. 354—359 // https://doi.org/10.1038/nature24270

1952

Silver D., Hubert T., Schrittwieser J., Antonoglou I., Lai M., Guez A., Lanctot M., Sifre L., Kumaran D., Graepel T., Lillicrap T., Simonyan K., Hassabis D. (2017). Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm // https://arxiv.org/abs/1712.01815

1953

Silver D., Hubert T., Schrittwieser J., Antonoglou I., Lai M., Guez A., Lanctot M., Sifre L., Kumaran D., Graepel T., Lillicrap T., Simonyan K., Hassabis D. (2018). A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play / Science, Vol. 362, Iss. 6419, pp. 1140—1144 // https://doi.org/10.1126/science.aar6404

1954

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1955

^* Эта история стала одной из причин того, почему я занялся популяризацией науки в области ИИ. Честно говоря, было больно читать и слушать откровенную ерунду вроде того, что сотни программистов, огромные команды, которые занимались шахматами, теперь не нужны, они теперь уволены. Проблема заключалась в том, что команды из сотен наёмных программистов, занимающиеся компьютерными шахматами, существовали только в воображении автора высказывания, да и сила игры Giraffe была на тот момент далека от силы игры лучших шахматных программ.

1956

Baxter J., Tridgell A., Weaver L. (1998). KnightCap: A chess program that learns by combining TD(λ) with game-tree search / Proc. 15th International Conf. on Machine Learning, pp. 28—36 // https://arxiv.org/abs/cs/9901002

1957

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1958

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1959

Kumar C., Fogel D. B. (1999). Evolution, Neural Networks, Games, and Intelligence / Proceedings of the IEEE, Vol. 87, Iss. 9, pp. 1471—1496 // https://doi.org/10.1109/5.784222

1960

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1961

Kumar C., Fogel D. B. (2001). Evolving an Expert Checkers Playing Program without Using Human Expertise / IEEE Transactions on Evolutionary Computation, Vol. 5, Iss. 4, pp. 422—428 // https://doi.org/10.1109/4235.942536

1962

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1963

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1964

Schrittwieser J., Antonoglou I., Hubert T., Simonyan K., Sifre L., Schmitt S., Guez A., Lockhart E., Hassabis D., Graepel T., Lillicrap T., Silver D. (2020). Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model // https://arxiv.org/abs/1911.08265

1965

Ye W., Liu S., Kurutach T., Abbeel P., Gao Y. (2021). Mastering Atari Games with Limited Data // https://arxiv.org/abs/2111.00210

1966

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1967

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1968

Wu D. J. (2019). Accelerating Self-Play Learning in Go // https://arxiv.org/abs/1902.10565

1969

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1970

Nasu Y. (2018). ƎUИИ: Efficiently Updatable Neural-Network-based Evaluation Functions for Computer Shogi // https://www.apply.computer-shogi.org/wcsc28/appeal/the_end_of_genesis_T.N.K.evolution_turbo_type_D/nnue.pdf

1971

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1972

Poundstone W. (2011). Prisoner's Dilemma. Knopf Doubleday Publishing Group // https://books.google.ru/books?id=twNXXfYVB1UC

1973

Bowling M., Burch N., Johanson M., Tammelin O. (2015). Heads-up Limit Hold’em Poker is Solved / Science, Vol. 347, Iss. 6218, pp. 145—149 // https://doi.org/10.1126/science.1259433

1974

Moravčík M., Schmid M., Burch N., Lisý V., Morrill D., Bard N., Davis T., Waugh K., Johanson M., Bowling M. (2017). DeepStack: Expert-level artificial intelligence in heads-up no-limit poker / Science, Vol. 356, Iss. 6337, pp. 508—513 // https://doi.org/10.1126/science.aam6960

1975

Mets C. (2017). Inside Libratus, the Poker AI That Out-Bluffed the Best Humans / Wired, 02.01.17 // https://www.wired.com/2017/02/libratus/

1976

Rodriguez J. (2019). Inside Pluribus: Facebook’s New AI That Just Mastered the World’s Most Difficult Poker Game / KDnuggets // https://www.kdnuggets.com/2019/08/inside-pluribus-facebooks-new-ai-poker.html

1977

Blair A., Saffidine A. (2019). AI surpasses humans at six-player poker / Science, Vol. 365, Iss. 6456, pp. 864–865 // https://doi.org/10.1126/science.aay7774

1978

Brown N., Lerer A., Gross S., Sandholm T. (2019). Deep Counterfactual Regret Minimization / Proceedings of the 36th International Conference on Machine Learning, PMLR 97:793-802 // http://proceedings.mlr.press/v97/brown19b.html

1979

Ontañón S., Synnaeve G., Uriarte A., Richoux F., Churchill D., Preuss M. (2013). A Survey of Real-Time Strategy Game AI Research and Competition in StarCraft / IEEE Transactions on Computational Intelligence and AI in Games, Vol. 5, No. 4, pp. 293—311 // https://doi.org/10.1109/TCIAIG.2013.2286295

1980

Schulman J., Klimov O., Wolski F., Dhariwal P., Radford A. (2017). Proximal Policy Optimization / OpenAI blog, July 20, 2017 // https://openai.com/blog/openai-baselines-ppo/

1981

Chan B., Tang J., Pondé H., Raiman J., Wolski F., Petrov M., Zhang S., Dennison C., Farhi D., Sidor S., Dębiak P., Pachocki J., Brockman G. (2018). OpenAI Five: Our team of five neural networks, OpenAI Five, has started to defeat amateur human teams at Dota 2 / OpenAI blog // https://openai.com/blog/openai-five/

1982

Matiisen T. (2018). The use of Embeddings in OpenAI Five / Computational Neuroscience Lab, Institute of Computer Science, University of Tartu, September 9, 2018 // https://neuro.cs.ut.ee/the-use-of-embeddings-in-openai-five/

1983

1984

OpenAI Five Defeats Dota 2 World Champions (2019) / OpenAI blog, April 15, 2019 // https://openai.com/blog/openai-five-defeats-dota-2-world-champions/

1985

Vinyals O., Babuschkin I., Chung J., Mathieu M., Jaderberg M., Czarnecki W., Dudzik A., Huang A., Georgiev P., Powell R., Ewalds T., Horgan D., Kroiss M., Danihelka I., Agapiou J., Oh J., Dalibard V., Choi D., Sifre L., Sulsky Y., Vezhnevets S., Molloy J., Cai T., Budden D., Paine T., Gulcehre C., Wang Z., Pfaff T., Pohlen T., Yogatama D., Cohen J., McKinney K., Smith O., Schaul T., Lillicrap T., Apps C., Kavukcuoglu K., Hassabis D., Silver D. (2019). AlphaStar: Mastering the Real-Time Strategy Game StarCraft II / DeepMind blog, 24 Jan 2019 // https://deepmind.com/blog/alphastar-mastering-real-time-strategy-game-starcraft-ii/

1986

Wünsch D. (2019) / Twitter // https://twitter.com/liquidtlo/status/1088524496246657030

1987

Solimito S. (2019). Is Alphastar really impressive? // https://medium.com/@stefano.solimito/is-alphastar-really-impressive-31ab02bf0882

1988

Kosker S. (2019). Künstliche Intelligenz gegen Mensch: DeepMind AlphaStar // https://stefankosker.com/alphastar-starcraft-deepmind-kuenstliche-intelligenz/#Prominente_Meinungen_zu_AlphaStar

1989

Lee T. B. (2019). An AI crushed two human pros at StarCraft—but it wasn’t a fair fight / Ars Technica // https://arstechnica.com/gaming/2019/01/an-ai-crushed-two-human-pros-at-starcraft-but-it-wasnt-a-fair-fight/

1990

SoulDrivenOlives (2019). DeepMind's PR regarding Alphastar is unbelievably bafflingg / Reddit // https://www.reddit.com/r/MachineLearning/comments/dr2vir/d_deepminds_pr_regarding_alphastar_is/

1991

Lee T. B. (2019). An AI crushed two human pros at StarCraft—but it wasn’t a fair fight. Superhuman speed and precision helped a StarCraft AI defeat two top players / Ars Technica, 1/30/2019 // https://arstechnica.com/gaming/2019/01/an-ai-crushed-two-human-pros-at-starcraft-but-it-wasnt-a-fair-fight/

1992

u/SoulDrivenOlives (2019).[D] An analysis on how AlphaStar's superhuman speed is a band-aid fix for the limitations of imitation learning / Reddit // https://www.reddit.com/r/MachineLearning/comments/ak3v4i/d_an_analysis_on_how_alphastars_superhuman_speed/

1993

Vinyals O., Babuschkin I., Czarnecki W. M., Mathieu M., Dudzik A., Chung J., Choi D. H., Powell R., Ewalds T., Georgiev P., Oh J., Horgan D., Kroiss M., Danihelka I., Huang A., Sifre L., Cai T., Agapiou J. P., Jaderberg M., Vezhnevets A. S., Leblond R., Pohlen T., Dalibard V., Budden D., Sulsky Y., Molloy J., Paine T. L., Gulcehre C., Wang Z., Pfaff T., Wu Y., Ring R., Yogatama D., Wünsch D., McKinney K., Smith O., Schaul T., Lillicrap T., Kavukcuoglu K., Hassabis D., Apps C., Silver D. (2019). Grandmaster level in StarCraft II using multi-agent reinforcement learning / Nature, Vol. 575, pp. 350–354 (2019) // https://doi.org/10.1038/s41586-019-1724-z

1994

^* Пер. М. Лозинского.

1995

Pandya D. A., Dennis B. H., Russell R. D. (2017). A computational fluid dynamics based artificial neural network model to predict solid particle erosion / Wear, Vol. 378—379, 15 May 2017, pp. 198—210 // https://doi.org/10.1016/j.wear.2017.02.028

1996

Kutz J. N. (2017). Deep learning in fluid dynamics / Journal of Fluid Mechanics, Vol. 814, 10 March 2017, pp. 1—4 // https://doi.org/10.1017/jfm.2016.803

1997

Zhang Y. G., Gajjar V., Foster G., Siemion A., Cordes J., Law C., Wang Y. (2018). Fast Radio Burst Pulse Detection and Periodicity: A Machine Learning Approach / The Astrophysical Journal, Vol. 866, No. 2 // https://doi.org/10.3847%2F1538-4357%2Faadf31

1998

Wei J. N., Duvenaud D., Aspuru-Guzik A. (2016). Neural Networks for the Prediction of Organic Chemistry Reactions / ACS Central Science, October 14, 2016, 2, 10, 725—732 // https://doi.org/10.1021/acscentsci.6b00219

1999

Rajpurkar P., Hannun A. Y., Haghpanahi M., Bourn C., Ng A. Y. (2017). Cardiologist-Level Arrhythmia Detection with Convolutional Neural Networks // https://arxiv.org/abs/1707.01836

2000

Schirrmeister R. T., Springenberg J. T., Fiederer L. D. J., Glasstetter M., Eggensperger K., Tangermann M., Hutter F., Burgard W., Ball T. (2017). Deep learning with convolutional neural networks for EEG decoding and visualization / Human Brain Mapping, Vol. 38, Iss. 11, November 2017, pp. 5391—5420 // https://doi.org/10.1002/hbm.23730

2001

Pyrkov T. V., Slipensky K., Barg M., Kondrashin A., Zhurov B., Zenin A., Pyatnitskiy M., Menshikov L., Markov S., Fedichev P. O. (2018). Extracting biological age from biomedical data via deep learning: too much of a good thing? / Scientific Reports, Vol. 8, Article num.: 5210 (2018) // https://doi.org/10.1038/s41598-018-23534-9

2002

Lin W., Tong T, Gao Q., Guo D., Du X., Yang Y., Guo G., Xiao M., Du M., Qu X. (2018). Convolutional Neural Networks-Based MRI Image Analysis for the Alzheimer’s Disease Prediction From Mild Cognitive Impairment / Frontiers in Neuroscience, 05 November 2018 // https://doi.org/10.3389/fnins.2018.00777

2003

^* Лидар (LIDAR, Light Detection and Ranging, обнаружение и определение дальности с помощью света) — технология измерения расстояний путём излучения света (лазер) и замера времени возвращения этого отражённого света на ресивер.

2004

Velas M., Spanel M., Hradis M., Herout A. (2018). CNN for very fast ground segmentation in velodyne LiDAR data / 2018 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC), Torres Vedras, 2018, pp. 97—103 // https://doi.org/10.1109/ICARSC.2018.8374167

2005

Martinsson E. (2017). WTTE-RNN: Weibull Time To Event Recurrent Neural Network. A model for sequential prediction of time-to-event in the case of discrete or continuous censored data, recurrent events or time-varying covariates. Master’s thesis in Engineering Mathematics & Computational Science // http://publications.lib.chalmers.se/records/fulltext/253611/253611.pdf

2006

Rebedea T. (2017). Deep Neural Networks for Matching Online Social Networking Profiles / Conference on Computational Collective Intelligence Technologies and Applications // https://doi.org/10.1007/978-3-319-67074-4_19

2007

Tan Q., Liu N., Hu X. (2019). Deep Representation Learning for Social Network Analysis / Frontiers in Big Data, 03 April 2019 // https://doi.org/10.3389/fdata.2019.00002

2008

Hamilton W. L, Ying R., Leskovec J. (2017). Representation Learning on Graphs: Methods and Applications / IEEE Data Engineering Bulletin // https://arxiv.org/abs/1709.05584

2009

Lample G., Charton F. (2019). Deep Learning for Symbolic Mathematics // https://arxiv.org/abs/1912.01412

2010

Palaskar S., Sanabria R., Metze F. (2018). End-to-End Multimodal Speech Recognition // https://arxiv.org/abs/1804.09713

2011

Nag N., Bharadwaj A., Rao A. N., Kulhalli A., Mehta K. S., Bhattacharya N., Ramkumar P., Sitaram D., Jain R. (2019). Flavour Enhanced Food Recommendation // https://arxiv.org/abs/1904.05331

2012

Lee B. K., Mayhew E. J., Sanchez-Lengeling B., Wei J. N., Qian W. W., Little K. A., Andres M., Nguyen B. B., Moloy T., Yasonik J., Parker J. K., Gerkin R. C., Mainland J. D., Wiltschko A. B. (2023). A principal odor map unifies diverse tasks in olfactory perception / Science, Vol. 381, pp. 999-1006 // https://doi.org/10.1126/science.ade4401

2013

Graves A., Wayne G., Danihelka I. (2014). Neural Turing Machines // https://arxiv.org/abs/1410.5401

2014

Graves A., Wayne G., Reynolds M., Harley T., Danihelka I., Grabska-Barwińska A., Colmenarejo S. G., Grefenstette E., Ramalho T., Agapiou J., Badia A. P., Hermann K. M., Zwols Y., Ostrovski G., Cain A., King H., Summerfield C., Blunsom P., Kavukcuoglu K., Hassabis D. (2016). Hybrid computing using a neural network with dynamic external memory / Nature, Vol. 538, pp. 471—476 (2016) // https://doi.org/10.1038/nature20101

2015

Collier M., Beel J. (2019). Memory-Augmented Neural Networks for Machine Translation // https://arxiv.org/abs/1909.08314

2016

^* Пер. Н. Россова.

2017

Шаврина Т. О. (2017). Методы обнаружения и исправления опечаток: исторический обзор / Вопросы языкознания. № 4. С. 115—134 // https://doi.org/10.31857/S0373658X0001024-5

2018

^* ^* ^* ^* ^* ^** ^** ^* Пер. П. Мелкова.

2019

Gardner W. D. (2008). Remembering Joe Weizenbaum, ELIZA Creator / InformationWeek // https://www.informationweek.com/remembering-joe-weizenbaum-eliza-creator-/d/d-id/1065648

2020

LordOmar (2000). AOLiza / everything2 // https://everything2.com/title/AOLiza

2021

Colby K. M., Hilf F. D., Weber S., Kraemer H. C. (1972). Turing-like indistinguishability tests for the validation of a computer simulation of paranoid processes / Artificial Intelligence, Vol., 1972, pp. 199—221 // https://doi.org/10.1016/0004-3702(72)90049-5

2022

Saygin A. P., Cicekli I., Akman V. (2003). Turing Test: 50 Years Later / Moor J. H. (2003). The Turing Test. The Elusive Standard of Artificial Intelligence. Studies in Cognitive Systems, Vol. 30, pp. 23–78 // https://doi.org/10.1007/978-94-010-0105-2_2

2023

Luiselli J. K., Fischer A. J. (2016). Computer-Assisted and Web-Based Innovations in Psychology, Special Education, and Health. Academic Press // https://books.google.ru/books?id=NwLSBgAAQBAJ

2024

Sussman G. J., Winograd T., Charniak E. (1971). Micro-Planner reference manual. Artificial Intelligence Memo No. 203A (Updates 203) // ftp://publications.ai.mit.edu/ai-publications/pdf/AIM-203a.pdf

2025

SHRDLU resurrection (2019) // http://maf.directory/misc/shrdlu.html

2026

Товарищ Силоч (@comrade_siloch) (2020) / Twitter // https://twitter.com/comrade_siloch/status/1217102334376976384

2027

Hutchins J. (2004). Two precursors of machine translation: Artsrouni and Trojanskij / International Journal of Translation, Vol. 16(1), January—June 2004, pp. 11—31 // http://www.hutchinsweb.me.uk/IJT-2004.pdf

2028

Kirjutusmafin-tõlk (1924) / Waba maa, Num. 46, 24 February 1924, p. 4 // https://dea.digar.ee/page/wabamaa/1924/02/24/4

2029

Kirjutusmafin-tõlk (1923) / Esmaspäev, 19 november 1923, p. 1 // https://dea.digar.ee/article/esmaspaev/1923/11/19/11

2030

2031

Богданов Н. В. Дружба / Богданов Н. В. (1958). О смелых и умелых // http://www.kulichki.com/moshkow/PRIKL/BOGDANOW/smelye.txt

2032

Nirenburg S., Somers H. L., Wilks Y. (2003). Readings in Machine Translation. MIT Press // https://books.google.ru/books?id=yx3lEVJMBmMC

2033

Hutchins J. (1995). “The whisky was invisible”, or Persistent myths of MT / MT News International 11 (June 1995), pp. 17—18 // http://www.hutchinsweb.me.uk/MTNI-11-1995.pdf

2034

Russell S. J., Norvig P. (2016). Artificial Intelligence: A Modern Approach. Pearson // https://books.google.ru/books?id=XS9CjwEACAAJ

2035

Hutchins J. (1997). From First Conception to First Demonstration: the Nascent Years of Machine Translation, 1947–1954. A Chronology / Machine Translation, Vol. 12 (3), pp. 195—252 // https://doi.org/10.1023/a:1007969630568

2036

Macdonald N. (1954). Language translation by machine — a report of the first successful trial / Computers and Automation, Vol. 3 (2), February 1954 // http://mt-archive.info/Macdonald-1954.pdf

2037

Henisz-Dostert B., Macdonald R. R., Zarechnak M. (2011). Machine Translation. Walter de Gruyter // https://books.google.ru/books?id=St4iXxXoIIAC

2038

701 Translator. IBM Press release, January 8, 1954 // http://www.mt-archive.info/IBM-1954.pdf

2039

Hutchins W. J. (2004). The Georgetown-IBM experiment demonstrated in January 1954 / Conference of the Association for Machine Translation in the Americas AMTA 2004: Machine Translation: From Real Users to Research, pp. 102—114 // https://doi.org/10.1007/978-3-540-30194-3_12

2040

Zarechnak M. (1959). Three Levels of Linguistic Analysis in Machine Translation / Journal of the ACM, January 1959 // https://doi.org/10.1145/320954.320956

2041

Hutchins W. J. (2000). Early Years in Machine Translation: Memoirs and biographies of pioneers. John Benjamins Publishing // https://books.google.ru/books?id=3dU5AAAAQBAJ

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Hutchins W. J. (1995). Machine translation: a brief history / Koerner E. F. K., Asher R. E. (1995). Concise history of the language sciences: from the Sumerians to the cognitivists. Oxford: Pergamon Press // http://hutchinsweb.me.uk/ConcHistoryLangSci-1995.pdf

2043

Hutchins J. (1996). ALPAC: the (in)famous report / MT News International, No. 14, June 1996, pp. 9—12 // http://www.hutchinsweb.me.uk/MTNI-14-1996.pdf

2044

Shapin S. (2015). Confusion of Tongues: Scientific Babel: The Language of Science from the Fall of Latin to the Rise of English by Michael Gordin / London Review of Books // https://www.lrb.co.uk/the-paper/v37/n23/steven-shapin/confusion-of-tongues

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Gordin M. (2015). Scientific Babel: The language of science from the fall of Latin to the rise of English. Profile Books // https://books.google.ru/books?id=2dmiBQAAQBAJ

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Hutchins J. (1996). ALPAC: the (in)famous report / MT News International, No. 14, June 1996, pp. 9—12 // http://www.hutchinsweb.me.uk/MTNI-14-1996.pdf

2047

Hutchins W. J. (1982). The evolution of machine translation systems / Lawson V. (1982). Practical experience of machine translation // http://www.mt-archive.info/Aslib-1981-Hutchins-1.pdf

2048

Вельмезова Е. (2015). Снова об универсалиях «лингвистическо-литературных»: «Структуральнейшая лингвистика» в повести А. и Б. Стругацких «Попытка к бегству» / Фаустов А. (2015). Универсалии русской литературы. Т. 6. — Воронеж: Издательско-полиграфический центр «Научная книга» // http://www.rusf.ru/abs/rec/velmez01.htm

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Мельчук И. А. (1984). Русский язык в модели смысл-текст / Russian Language Journal, Vol. 38, Iss. 129/130, pp. 189—198 // https://codenlp.ru/knigi/russkiy-yazyik-v-modeli-smyisl-tekst-melchuk.html

2050

^* Функционализм (функциональный структурализм, функциональная лингвистика) — совокупность школ и направлений, возникших как одно из ответвлений структурной лингвистики; характеризуется фокусом на функционировании языка как средства общения. Изначальный импульс развития функционализм получил в «Тезисах Пражского лингвистического кружка» (1929), а затем был развит в работах представителей Пражской лингвистической школы.

2051

Алпатов В. М. (2005). История лингвистических учений. Учебное пособие / 4-е изд., исправ. и доп. — М.: Языки славянской культуры // http://genling.spbu.ru/hl/085.pdf

2052

Ярцева В. Н. (1990). Лингвистический энциклопедический словарь. — М.: Советская энциклопедия // http://tapemark.narod.ru/les/index.html

2053

Алпатов В. М. (2005). История лингвистических учений / 4-е изд., исправ. и доп. — М.: Языки славянской культуры // http://genling.spbu.ru/hl/085.pdf

2054

2055

de Saussure F., Riedlinger A. Course in General Linguistics. Translated by Wade Baskin. Philosophical Library // https://books.google.ru/books?id=MCdZAAAAMAAJ

2056

Berger A. A. (2018). Media Analysis Techniques. SAGE Publications // https://books.google.ru/books?id=kbVItAEACAAJ

2057

de Saussure F., Riedlinger A. Course in General Linguistics. Translated by Wade Baskin. Philosophical Library // https://books.google.ru/books?id=MCdZAAAAMAAJ

2058

2059

Лукин О. В. (2015). История языкознания с VI в. до н. э. до середины XX в. Учебное пособие // http://yspu.org/images/4/48/История_языкознания.pdf

2060

Galofaro F. (2013). Formalizing Narrative Structures: Glossematics, Generativity, and Transformational Rules / Signata, No. 4, 2013, p. 227-246 // https://doi.org/10.4000/signata.757

2061

Seuren P. (1998). Western Linguistics: An Historical Introduction. Wiley // https://books.google.ru/books?id=YD7fupu-qS0C

2062

Sova R. (2006). Genesis of Two Algebraic Theories of Language / Linguistica ONLINE, January, 30th 2006 // http://www.phil.muni.cz/linguistica/art/sova/sov-001.pdf

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Chomsky N. (1975). The Logical Structure of Linguistic Theory. Springer US // https://books.google.ru/books?id=1D66ktXOITAC

2064

Seuren P. (1998). Western Linguistics: An Historical Introduction. Wiley // https://books.google.ru/books?id=YD7fupu-qS0C

2065

Graffi G. (2017). Harris, Chomsky and the origins of transformational grammar / Lingvisticæ Investigationes, Vol. 39, Iss. 1, Dec 2016, pp. 48—87 // https://doi.org/10.1075/li.39.1.03gra

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Louwerse M. (2021). Keeping Those Words in Mind: How Language Creates Meaning. Rowman & Littlefield // https://books.google.ru/books?id=gbcmEAAAQBAJ

2067

Miller G. A. (2003). The cognitive revolution: a historical perspective / TRENDS in Cognitive Sciences, Vol. 7, No.3, March 2003 // https://www.cs.princeton.edu/~rit/geo/Miller.pdf

2068

Davis M. D., Sigal R., Weyuker E. J. (1994). Computability, Complexity, and Languages: Fundamentals of Theoretical Computer Science (2nd ed.). Boston: Academic Press, Harcourt, Brace // https://books.google.ru/books?id=6G_arEqHtysC

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Chomsky N. (1965). Aspects of the Theory of Syntax. MIT Press // https://books.google.ru/books?id=SOtsAAAAIAAJ

2070

Fodor J. A. (1983). The Modularity of Mind: An Essay on Faculty Psychology // https://books.google.ru/books?id=e7nrSeibJZYC

2071

^* Иногда также используется термин «Упорядоченное психическое представление мыслей» (Thought ordered mental expression, TOME).

2072

Fodor J. A. (1975). The Language of Thought // https://books.google.ru/books?id=XZwGLBYLbg4C

2073

Лагунина И., Ольшанская Е. (2004). Машинный перевод / Радио Свобода, 21 января // https://www.svoboda.org/a/24196111.html

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Лаборатория №15. Компьютерная лингвистика / Российская академия наук. Институт проблем передачи информации им. А. А. Харкевича // http://iitp.ru/ru/researchlabs/245.htm

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Галактионов В. А., Мусатов А. М., Мансурова О. Ю., Ёлкин С. В., Клышинский Э. С., Максимов В. Ю., Аминева С. Н., Жирнов Р. В., Игашов С. Ю., Мусаева Т. Н. (2007). Система машинного перевода «Кросслятор 2.0» и анализ её функциональности для задачи трансляции знаний // https://www.keldysh.ru/papers/2007/prep89/prep2007_89.html

2076

Hutchins W. J. (2000). Early Years in Machine Translation: Memoirs and biographies of pioneers. John Benjamins Publishing // https://books.google.ru/books?id=3dU5AAAAQBAJ

2077

Loh S.-C., Kong L., Hung H.-S. (1978). Machine translation of Chinese mathematical articles / ALLC Bulltein, Vol. 6(2), pp. 111—120 // http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.598.8762&rep=rep1&type=pdf

2078

Hutchins W. J. (2000). Early Years in Machine Translation: Memoirs and biographies of pioneers. John Benjamins Publishing // https://books.google.ru/books?id=3dU5AAAAQBAJ

2079

Chan S. (2004). A Dictionary of Translation Technology. Chinese University Press // https://books.google.ru/books?id=3gwOFvbxMGcC

2080

Лаздинь Т. А. (2009). Основы статистической оптимизации преподавания иностранных языков / Вестник СПбГУ. Язык и литература. 2009. № 3 // https://cyberleninka.ru/article/n/osnovy-statisticheskoy-optimizatsii-prepodavaniya-inostrannyh-yazykov

2081

Зубов А. В., Носкова Т. Н. (2017). Р. Г. Пиотровский — основатель компьютерной лингвистики в Беларуси / Пиотровские чтения 2017 // http://ceur-ws.org/Vol-2233/

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Пиотровский Р. Г. (1980). Статистика речи и автоматический анализ текста. — Л.: Наука // https://www.phantastike.com/linguistics/statistika_rechi_1980/pdf/

2083

История машинного перевода: от гипотез Лейбница и Декарта — до мобильных приложений и облачных сервисов (2019) / PROMT // https://www.promt.ru/press/blog/istoriya-mashinnogo-perevoda-ot-gipotez-leybnitsa-i-dekarta-do-mobilnykh-prilozheniy-i-oblachnykh-se/

2084

NLLB Team, Costa-jussà M. R., Cross J., Çelebi O., Elbayad M., Heafield K., Heffernan K., Kalbassi E., Lam J., Licht D., Maillard J., Sun A., Wang S., Wenzek G., Youngblood A., Akula B., Barrault L., Gonzalez G. M., Hansanti P., Hoffman J., Jarrett S., Sadagopan K. R., Rowe D., Spruit S., Tran C., Andrews P., Ayan N. F., Bhosale S., Edunov S., Fan A., Gao C., Goswami V., Guzmán F., Koehn P., Mourachko A., Ropers C., Saleem S., Schwenk H., Wang J. (2022). No Language Left Behind: Scaling Human-Centered Machine Translation // https://arxiv.org/abs/2207.04672

2085

Fan A., Bhosale S., Schwenk H., Ma Z., El-Kishky A., Goyal S., Baines M., Celebi O., Wenzek G., Chaudhary V., Goyal N., Birch T., Liptchinsky V., Edunov S., Grave E., Auli M., Joulin A. (2020). Beyond English-Centric Multilingual Machine Translation // https://arxiv.org/abs/2010.11125

2086

Seamless Communication, Barrault L., Chung Y., Meglioli M. C., Dale D., Dong N., Duquenne P., Elsahar H., Gong H., Heffernan K., Hoffman J., Klaiber C., Li P., Licht D., Maillard J., Rakotoarison A., Sadagopan K. R., Wenzek G., Ye E., Akula B., Chen P., Hachem N. E., Ellis B., Gonzalez G. M., Haaheim J., Hansanti P., Howes R., Huang B., Hwang M., Inaguma H., Jain S., Kalbassi E., Kallet A., Kulikov I., Lam J., Li D., Ma X., Mavlyutov R., Peloquin B., Ramadan M., Ramakrishnan A., Sun A., Tran K., Tran T., Tufanov I., Vogeti V., Wood C., Yang Y., Yu B., Andrews P., Balioglu C., Costa-jussà M. R., Celebi O., Elbayad M., Gao C., Guzmán F., Kao J., Lee A., Mourachko A., Pino J., Popuri S., Ropers C., Saleem S., Schwenk H., Tomasello P., Wang C., Wang J., Wang S. (2023). SeamlessM4T-Massively Multilingual & Multimodal Machine Translation // https://aps.arxiv.org/abs/2308.11596

2087

Papineni K., Roukos S., Ward T., Zhu W.-J. (2002). BLEU: a Method for Automatic Evaluation of Machine Translation / Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311—317 // https://doi.org/10.3115/1073083.1073135

2088

Snover M., Dorr B., Schwartz R., Micciulla L., Makhoul J. (2006). A Study of Translation Edit Rate with Targeted Human Annotation / Proceedings of Association for Machine Translation in the Americas, 2006, pp. 223—231 // http://mt-archive.info/AMTA-2006-Snover.pdf

2089

Chen B., Kuhn R. (2011). AMBER: a modified BLEU, enhanced ranking metric / WMT '11: Proceedings of the Sixth Workshop on Statistical Machine Translation, July 2011, pp. 71—77 // https://www.aclweb.org/anthology/W11-2105/

2090

Banerjee S., Lavie A. (2005). METEOR: An Automatic Metric for MT Evaluation with Improved Correlation with Human Judgments / Proceedings of the ACL 2005 Workshop on Intrinsic and Extrinsic Evaluation Measures for MT and/or Summarization // https://www.aclweb.org/anthology/W05-0909/

2091

Han A. L.-F. (2017). LEPOR: An Augmented Machine Translation Evaluation Metric // https://arxiv.org/abs/1703.08748

2092

Han A. L.-F., Wong D. F., Chao L. S., He L., Lu Y. (2014). Unsupervised Quality Estimation Model for English to German Translation and Its Application in Extensive Supervised Evaluation / The Scientific World Journal, Vol. 2014 // https://doi.org/10.1155/2014/760301

2093

Aaron Li-Feng Han A. L.-F., Wong D. F., Chao L. S., He L., Lu Y., Xing J., Zeng X. (2013). Language-independent Model for Machine Translation Evaluation with Reinforced Factors / Proceedings of the XIV Machine Translation Summit (Nice, September 2–6, 2013), pp. 215—222 // http://www.mt-archive.info/10/MTS-2013-Han.pdf

2094

Lin C.-Y. (2004). ROUGE: a Package for Automatic Evaluation of Summaries / Proceedings of the Workshop on Text Summarization Branches Out (WAS 2004), Barcelona, Spain, July 25—26, 2004 // https://www.aclweb.org/anthology/W04-1013/

2095

Zhang T., Kishore V., Wu F., Weinberger K. Q., Artzi Y. (2020). BERTScore: Evaluating Text Generation with BERT // https://arxiv.org/abs/1904.09675

2096

Marie B. (2022). BLEU: A Misunderstood Metric from Another Age But still used today in AI research / Towards Data Science, Nov 5, 2022. // https://towardsdatascience.com/bleu-a-misunderstood-metric-from-another-age-d434e18f1b37

2097

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Callison-Burch C., Koehn P., Monz C., Post M., Soricut R., Specia L. (2013). Findings of the 2012 Workshop on Statistical Machine Translation / NAACL 2012 Seventh Workshop on Statistical Machine Translation // http://www.statmt.org/wmt12/pdf/WMT02.pdf

2099

Gao P., He Z., Wu H., Wang H. (2022). Bi-SimCut: A Simple Strategy for Boosting Neural Machine Translation // https://arxiv.org/abs/2206.02368

2100

Wei J., Bosma M., Zhao V. Y., Guu К., Yu A. W., Lester B., Du N., Dai A. M., Le Q. V. (2021). Finetuned Language Models Are Zero-Shot Learners // https://arxiv.org/abs/2109.01652

2101

Liang X., Wu L., Li J., Wang Y., Meng Q., Qin T., Chen W., Zhang M., Liu T.-Y. (2020). R-Drop: Regularized Dropout for Neural Networks // https://arxiv.org/abs/2106.14448

2102

Лагунина И., Ольшанская Е. (2004). Машинный перевод / Радио Свобода, 21 января // https://www.svoboda.org/a/24196111.html

2103

Savenkov K. (2018). State of the machine translation by Intento (2018) // https://www.slideshare.net/KonstantinSavenkov/state-of-the-machine-translation-by-intento-july-2018

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Savenkov K. (2019). State of the Machine Translation (January 2019) / Intento, Mar 9, 2019 // https://blog.inten.to/state-of-the-machine-translation-january-2019-dffe15884d63

2105

Savenkov K. (2019). State of the Machine Translation (June 2019) // Intento, Jun 20, 2019 // https://blog.inten.to/state-of-the-machine-translation-june-2019-e3ffb457b76c

2106

The State of Machine Translation 2020. Independent multi-domain evaluation of commercial Machine Translation engines (2020) / Intento // https://try.inten.to/mt_report_2020

2107

Stanford Human-Centered Artificial Intelligence (HAI) (2019). Artificial Intelligence Index Report 2019 // https://hai.stanford.edu/sites/default/files/ai_index_2019_report.pdf

2108

Manning C. D., Raghavan P., Schütze H. (2008). Introduction to Information Retrieval. Cambridge University Press // https://books.google.ru/books?id=t1PoSh4uwVcC

2109

Bengio Y., Ducharme R., Vincent P., Jauvin C. (2003). A Neural Probabilistic Language Model / Journal of Machine Learning Research, Vol. 3 (2003), pp. 1137—1155 // http://www.jmlr.org/papers/volume3/bengio03a/bengio03a.pdf

2110

Francis W. N., Kucera H. (1979). Brown corpus manual. Manual of information to accompany a standard corpus of present-day edited American English, for use with digital computers // http://korpus.uib.no/icame/brown/bcm.html

2111

Wikipedia contributors. (2021, August 2). Size of Wikipedia. In Wikipedia, The Free Encyclopedia. Retrieved 08:00, August 2, 2021, from https://en.wikipedia.org/wiki/Wikipedia:Size_of_Wikipedia

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Marcus M. P., Santorini B., Marcinkiewicz M. A. (1993). Building a Large Annotated Corpus of English: The Penn Treebank / Computational Linguistics, Vol. 19, Iss. 2 // https://aclanthology.org/J93-2004/

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BNC Consortium (2007). The British National Corpus, XML Edition. Oxford Text Archive // http://hdl.handle.net/20.500.12024/2554

2114

Burnard L. (2007). Reference Guide for the British National Corpus (XML Edition) // http://www.natcorp.ox.ac.uk/XMLedition/URG/

2115

BNC Consortium (2001). The British National Corpus, version 2 (BNC World) // http://www.natcorp.ox.ac.uk/

2116

Burnard L. (2000). The British National Corpus Users Reference Guide // http://www.natcorp.ox.ac.uk/docs/userManual/

2117

Harris Z. (1954). Distributional structure / Word, Vol. 10, Iss. 23, pp. 146—162 // https://doi.org/10.1080/00437956.1954.11659520

2118

^* Корпусная лингвистика — раздел языкознания, занимающийся разработкой, созданием и использованием текстовых корпусов.

2119

Wallis S. (2016). Why Chomsky was Wrong About Corpus Linguistics / corp.ling.stats: statistics for corpus linguists, November 2, 2016 // https://corplingstats.wordpress.com/2016/11/02/why-chomsky-was-wrong/

2120

Firth J. R. (1957). A synopsis of linguistic theory 1930-1955 // https://books.google.ru/books?id=T8LDtgAACAAJ

2121

Maruyama Y. (2019). Quantum Physics and Cognitive Science from a Wittgensteinian Perspective: Bohr’s Classicism, Chomsky’s Universalism, and Bell’s Contextualism / Wuppuluri S., da Costa N. (2019). WITTGENSTEINIAN (adj.). The Frontiers Collection. Springer, Cham // https://doi.org/10.1007/978-3-030-27569-3_20

2122

Kilgarriff A., Baisa V., Bušta J., Jakubíček M., Kovář V., Michelfeit J., Rychlý P., Suchomel V. (2014). The Sketch Engine: ten years on / Lexicography, Vol. 1, Iss. 1, pp. 7–36 // https://doi.org/10.1007/s40607-014-0009-9

2123

^* Диахрония (от греч. δια — через, сквозь и χρονος — время) — рассмотрение исторического развития языковых явлений и языковой системы как предмета лингвистического исследования. Противопоставляется синхронии (от греч. συν — совместно и χρονος — время) — рассмотрение состояния языка как установившейся системы в определённый момент времени.

2124

Mnih A., Hinton G. E. (2009). A scalable hierarchical distributed language model / Advances in neural information processing systems, Vol. 21, pp. 1081—1088 // https://papers.nips.cc/paper/3583-a-scalable-hierarchical-distributed-language-model

2125

Mnih A., Teh Y. W. (2012). A fast and simple algorithm for training neural probabilistic language models // Proceedings of the 29th International Coference on International Conference on Machine Learning, pp. 419—426 // https://arxiv.org/abs/1206.6426

2126

Collobert R., Weston J. (2008). A unified architecture for natural language processing: deep neural networks with multitask learning / Proceedings of the 25th international conference on Machine learning, pp. 160—167 // https://doi.org/10.1145/1390156.1390177

2127

Turian J., Ratinov L., Bengio Y. (2010). Word representations: a simple and general method for semi-supervised learning / Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pp. 384—394 // https://dl.acm.org/doi/10.5555/1858681.1858721

2128

Mikolov T., Chen K., Corrado G., Dean J. (2013). Efficient Estimation of Word Representations in Vector Space / International Conference on Learning Representations (ICLR-2013) // https://arxiv.org/abs/1301.3781

2129

Mikolov T., Sutskever I., Chen K., Corrado G., Dean J. (2013). Distributed Representations of Words and Phrases and their Compositionality / Proceedings of the 26th International Conference on Neural Information Processing Systems, Vol. 2, pp. 3111—3119 // https://papers.nips.cc/paper/5021-distributed-representations-of-words-and-phrases-and-their-compositionality.pdf

2130

2131

Mikolov T., Kombrink S., Deoras A., Burget L, Černocký J. (2011). RNNLM — Recurrent Neural Network Language Modeling Toolkit / Proceedings of IEEE Automatic Speech Recognition and Understanding Workshop, 2011, pp. 1—4 // https://www.fit.vut.cz/research/publication/10087/.en

2132

Wilson B., Schakel A. M. J. (2015). Controlled Experiments for Word Embeddings // https://arxiv.org/abs/1510.02675

2133

Rajasekharan A. (2017). How does word2vec work? Can someone walk through a specific example? / Quora // https://www.quora.com/How-does-word2vec-work-Can-someone-walk-through-a-specific-example/answer/Ajit-Rajasekharan

2134

Gong C., He D., Tan X., Qin T., Wang L., Liu T.-Y. (2020). FRAGE: Frequency-Agnostic Word Representation // https://arxiv.org/abs/1809.06858

2135

2136

İrsoy O., Benton A., Stratos K. (2020). kōan: A Corrected CBOW Implementation // https://arxiv.org/abs/2012.15332

2137

Сапунов Г. (2021). kōan: A Corrected CBOW Implementation (Ozan İrsoy, Adrian Benton, Karl Stratos) / gonzo-обзоры ML статей, Jan 19, 2021 // https://t.me/gonzo_ML/452

2138

^* Социальное познание (англ. social cognition) — процесс познания одного человека другим, одна из сфер, изучаемых социальной психологией, которая исследует механизмы хранения, переработки и использования человеком информации о других людях и социальных ситуациях.

2139

^** Организационное поведение (англ. organizational behavior) — научная дисциплина, занимающаяся исследованием поведения людей в организациях.

2140

Richie R., Zou W., Bhatia S., Vazire S. (2019). Predicting High-Level Human Judgment Across Diverse Behavioral Domains / Psychology, Vol. 5, Iss. 1, p. 50 // https://doi.org/10.1525/collabra.282

2141

Baroni M., Dinu G., Kruszewski G. (2014). Don’t count, predict! A systematic comparison of context-counting vs. context-predicting semantic vectors / Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) // https://doi.org/10.3115/v1/P14-1023

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Sivakumar S., Videla L. S., Rajesh Kumar T., Nagaraj J., Itnal S., Haritha D. (2020). Review on Word2Vec Word Embedding Neural Net / 2020 International Conference on Smart Electronics and Communication (ICOSEC) // https://doi.org/10.1109/icosec49089.2020.9215319

2143

Adewumi T. P., Liwicki F., Liwicki M. (2020). Word2Vec: Optimal Hyper-Parameters and Their Impact on NLP Downstream Tasks // https://arxiv.org/abs/2003.11645

2144

Pennington J., Socher R., Manning C. (2014). GloVe: Global Vectors for Word Representation / Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543. // https://doi.org/10.3115/v1/D14-1162

2145

Bojanowski P., Grave E., Joulin A., Mikolov T. (2016). Enriching Word Vectors with Subword Information // https://arxiv.org/abs/1607.04606

2146

Peters M. E., Neumann M., Iyyer M., Gardner M., Clark C., Lee K., Zettlemoyer L. (2018). Deep contextualized word representations // https://arxiv.org/abs/1802.05365

2147

Sales J. E., Souza L., Barzegar S., Davis B., Freitas A., Handschuh S. (2018). Indra: A Word Embedding and Semantic Relatedness Server / Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018) // https://aclanthology.org/L18-1211/

2148

Asgari E., Mofrad M. R. K. (2015). ProtVec: A Continuous Distributed Representation of Biological Sequences // https://arxiv.org/abs/1503.05140

2149

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^* Анафора (от греч. ἀναφέρειν — относить назад, возвращать, возводить к чему-либо) — зависимость интерпретации выражения от другого (обычно предшествующего) выражения в тексте.

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^* Здесь мы сознательно не углубляемся в вопрос, какие именно люди включаются в контрольную группу для оценки способности человека решать те или иные задачи, связанные с пониманием естественного языка (да и вообще любые другие интеллектуальные задачи в ситуациях, когда мы хотим сравнить способности машин и людей). Очевидно, что в идеале состав контрольной группы должен быть достаточно репрезентативным: включать в себя людей с разным уровнем образования, с разными профессиями, принадлежащих к разным социальным группам и культурным общностям. На практике, конечно, формируемые исследователями контрольные группы весьма далеки от идеала. Анализу этой проблемы посвящена весьма поучительная работа исследователей из Гарвардского университета под красноречивым названием «Какие люди?» [Which humans?].

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^* Данное слово может быть переведено на русский язык как «недоумение» или «растерянность», что неплохо отражает смысл этой метрики.

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^* Этот метод оценки получил название Acute-eval [«Острая» или «умная» оценка].

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^* В тоновых языках высота звука является смыслоразличительной компонентой; различные тоновые единицы, имеющие смыслоразличительную функцию в таких языках, иногда называют тонемами по аналогии с фонемами; к числу тоновых относятся китайский и некоторые другие азиатские языки.

2342

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^* Разговорное название конструкторских бюро, в которых работали осуждённые учёные и инженеры.

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^* Пер. Веры Набоковой.

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Sanyal S. (2022). Sentient AI has Hired a Lawyer to Fight its Legal Battles! Beware. / Analytics Insight, June 22, 2022 // https://www.analyticsinsight.net/sentient-ai-has-hired-a-lawyer-to-fight-its-legal-battles-beware/

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Levy S. (2022). Blake Lemoine Says Google's LaMDA AI Faces 'Bigotry'. / Wired, Jun 17, 2022 // https://www.wired.com/story/blake-lemoine-google-lamda-ai-bigotry/

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Tiku N. (2022). Google fired engineer who said its AI was sentient. / The Washington Post, July 22, 2022 // https://www.washingtonpost.com/technology/2022/07/22/google-ai-lamda-blake-lemoine-fired/

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Lemoine B (2022). Is LaMDA Sentient? — an Interview // https://cajundiscordian.medium.com/is-lamda-sentient-an-interview-ea64d916d917

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By Invitation (2022). Artificial neural networks are making strides towards consciousness, according to Blaise Agüera y Arcas. / The Economist, June 11th 2022 // https://www.economist.com/by-invitation/2022/09/02/artificial-neural-networks-are-making-strides-towards-consciousness-according-to-blaise-aguera-y-arcas

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Butlin P., Long R., Elmoznino E., Bengio Y., Birch J., Constant A., Deane G., Fleming S. M., Frith C., Ji X., Kanai R., Klein C., Lindsay G., Michel M., Mudrik L., Peters M. A. K., Schwitzgebel E., Simon J., VanRullen R. (2023). Consciousness in Artificial Intelligence: Insights from the Science of Consciousness // https://arxiv.org/abs/2308.08708

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^* Агентность — способность выступать в качестве агента, способность к самостоятельному действию. Понятие агента вводилось в начале книги, например как «интеллектуальный агент — любое устройство, которое воспринимает своё окружение и осуществляет действия, максимизирующие шансы успешного достижения его целей» или «агент — это просто нечто, что осуществляет действия (слово происходит от лат. agere, что значит „делать“)».

2433

Marcus G. (2023). / Twitter, 21 авг. 2023 // https://twitter.com/GaryMarcus/status/1693664792324841686

2434

Social AI and Extended Intelligence. MIT Media Lab // https://www.media.mit.edu/projects/social-ai-and-extended-intelligence/overview/

2435

^* Здесь необходимо сделать ещё одно важное терминологическое пояснение. Хотя в отечественной традиции термин affective computing принято переводить именно как «эмоциональные вычисления», есть некоторая разница между эмоцией и аффектом, про которую важно не забывать в дальнейших рассуждениях. Термином affect (от лат. affectus — воля, намерение; также — любовь, расположение, пристрастие) в английском языке обычно обозначают субъективный аспект эмоции — либо её психическую сторону, взятую в отрыве от объективных физиологических реакций, либо набор наблюдаемых поведенческих проявлений этой субъективно переживаемой эмоции. Выбор этого термина подчёркивает в данном случае, что данная дисциплина делает основной акцент на обработке эмоциональной информации, а не на анализе физиологических коррелятов человеческих эмоций, то есть физиологических процессов, наблюдаемых при переживании человеком эмоции. Эмоционально окрашенная речь, представленная в виде текста, безусловно может быть предметом обработки в системах ИЭИ, хотя в ней и не содержится сведений о физиологических процессах, происходивших в организме человека, в момент написания этого текста. Однако термин «аффективные вычисления» будет, скорее всего, непонятен неспециалистам, не задумывающимся над терминологическими тонкостями. В русском языке термин «аффект» является более многозначным, чем в английском. Например, под аффектом (или состоянием аффекта) понимают кратковременное эмоциональное состояние человека, в котором он считается невменяемым или ограниченно вменяемым. В английском языке для этого состояния используется понятие irresistible impulse (дословно: «непреодолимый импульс»). В силу этого, термин «аффективные вычисления» будет скорее запутывать читателя, чем служить делу уточнения смысла. Поэтому вслед за другими русскоязычными авторами я буду использовать термин «эмоциональные вычисления».

2436

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^* Поскольку благодаря появлению трансформерной архитектуры возник ряд моделей с числом параметров, превышающим 100 миллионов, для обозначения таких моделей в научной литературе стали применять специальный термин — «большие языковые модели» (Large Language Model, LLM). Конечно, само значение в 100 миллионов параметров является весьма условным (в некоторых источниках вы найдёте другие значения этой границы, например 1 млрд параметров), поэтому в отношении некоторых моделей могут возникнуть сомнения: считать их большими или нет. Но с практической точки зрения эти споры вряд ли представляют какой-либо интерес.

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^* Чего стоит хотя бы такой пассаж: «Кроме того, Пугачёва раскрыла подробности своей биографии, в которой оказалось немало скандальных эпизодов. Например, она утверждала, что в молодости была гейшей, а также что у неё в шкафу хранился сухой паёк на случай атомной войны, а её зять Г. Л. Рамазанов открыл для себя ясновидение».

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^* BaGuaLu (八卦炉), печь восьми триграмм (восьми гуа), волшебная печь из древнекитайской мифологии, позволяющая создавать эффективные лекарства. Восемь триграмм гуа используются в даосской космологии, чтобы представить фундаментальные принципы бытия.

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Chowdhery A., Narang S., Devlin J., Bosma M., Mishra G., Roberts A., Barham P., Chung H. W., Sutton C., Gehrmann S., Schuh P., Shi K., Tsvyashchenko S., Maynez J., Rao A., Barnes P., Tay Y., Shazeer N., Prabhakaran V., Reif E., Du N., Hutchinson B., Pope R., Bradbury J., Austin J., Isard M., Gur-Ari G., Yin P., Duke T., Levskaya A., Ghemawat S., Dev S., Michalewski H., Garcia X., Misra V., Robinson K., Fedus L., Zhou D., Ippolito D., Luan D., Lim H., Zoph B., Spiridonov A., Sepassi R., Dohan D., Agrawal S., Omernick M., Dai A. M., Pillai T. S., Pellat M., Lewkowycz A., Moreira E., Child R., Polozov O., Lee K., Zhou Z., Wang X., Saeta B., Diaz M., Firat O., Catasta M., Wei J., Meier-Hellstern K., Eck D., Dean J., Petrov S., Fiedel N. (2022). PaLM: Scaling Language Modeling with Pathways // https://arxiv.org/abs/2204.02311

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Anil R., Dai A. M., Firat O., Johnson M., Lepikhin D., Passos A., Shakeri S., Taropa E., Bailey P., Chen Z., Chu E., Clark J. H., Shafey L. E., Huang Y., Meier-Hellstern K., Mishra G., Moreira E., Omernick M., Robinson K., Ruder S., Tay Y., Xiao K., Xu Y., Zhang Y., Abrego G. H., Ahn J., Austin J., Barham P., Botha J., Bradbury J., Brahma S., Brooks K., Catasta M., Cheng Y., Cherry C., Choquette-Choo C. A., Chowdhery A., Crepy C., Dave S., Dehghani M., Dev S., Devlin J., Díaz M., Du N., Dyer E., Feinberg V., Feng F., Fienber V., Freitag M., Garcia X., Gehrmann S., Gonzalez L., Gur-Ari G., Hand S., Hashemi H., Hou L., Howland J., Hu A., Hui J., Hurwitz J., Isard M., Ittycheriah A., Jagielski M., Jia W., Kenealy K., Krikun M., Kudugunta S., Lan C., Lee K., Lee B., Li E., Li M., Li W., Li Y., Li J., Lim H., Lin H., Liu Z., Liu F., Maggioni M., Mahendru A., Maynez J., Misra V., Moussalem M., Nado Z., Nham J., Ni E., Nystrom A., Parrish A., Pellat M., Polacek M., Polozov A., Pope R., Qiao S., Reif E., Richter B., Riley P., Ros A. C., Roy A., Saeta B., Samuel R., Shelby R., Slone A., Smilkov D., So D. R., Sohn D., Tokumine S., Valter D., Vasudevan V., Vodrahalli K., Wang X., Wang P., Wang Z., Wang T., Wieting J., Wu Y., Xu K., Xu Y., Xue L., Yin P., Yu J., Zhang Q., Zheng S., Zheng C., Zhou W., Zhou D., Petrov S., Wu Y. (2023). PaLM 2 Technical Report // https://arxiv.org/abs/2305.10403

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Dettmers T., Pagnoni A., Holtzman A., Zettlemoyer L. (2023). QLoRA: Efficient Finetuning of Quantized LLMs // https://arxiv.org/abs/2305.14314

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Geng X., Gudibande A., Liu H., Wallace E., Abbeel P., Levine S., Song D. (2023). Koala: A Dialogue Model for Academic Research // https://bair.berkeley.edu/blog/2023/04/03/koala/

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Patil S. G., Zhang T., Wang X., Gonzalez J. E. (2023). Gorilla: Large Language Model Connected with Massive APIs // https://arxiv.org/abs/2305.15334

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Mukherjee S., Mitra A., Jawahar G., Agarwal s., Palangi H., Awadallah A. (2023). Orca: Progressive Learning from Complex Explanation Traces of GPT-4 // https://arxiv.org/abs/2306.02707

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Stability AI (2023). Meet Stable Beluga 1 and Stable Beluga 2, Our Large and Mighty Instruction Fine-Tuned Language Models. // https://stability.ai/blog/stable-beluga-large-instruction-fine-tuned-models

2658

Anil R., Dai A. M., Firat O., Johnson M., Lepikhin D., Passos A., Shakeri S., Taropa E., Bailey P., Chen Z., Chu E., Clark J. H., Shafey L. E., Huang Y., Meier-Hellstern K., Mishra G., Moreira E., Omernick M., Robinson K., Ruder S., Tay Y., Xiao K., Xu Y., Zhang Y., Abrego G. H., Ahn J., Austin J., Barham P., Botha J., Bradbury J., Brahma S., Brooks K., Catasta M., Cheng Y., Cherry C., Choquette-Choo C. A., Chowdhery A., Crepy C., Dave S., Dehghani M., Dev S., Devlin J., Díaz M., Du N., Dyer E., Feinberg V., Feng F., Fienber V., Freitag M., Garcia X., Gehrmann S., Gonzalez L., Gur-Ari G., Hand S., Hashemi H., Hou L., Howland J., Hu A., Hui J., Hurwitz J., Isard M., Ittycheriah A., Jagielski M., Jia W., Kenealy K., Krikun M., Kudugunta S., Lan C., Lee K., Lee B., Li E., Li M., Li W., Li Y., Li J., Lim H., Lin H., Liu Z., Liu F., Maggioni M., Mahendru A., Maynez J., Misra V., Moussalem M., Nado Z., Nham J., Ni E., Nystrom A., Parrish A., Pellat M., Polacek M., Polozov A., Pope R., Qiao S., Reif E., Richter B., Riley P., Ros A. C., Roy A., Saeta B., Samuel R., Shelby R., Slone A., Smilkov D., So D. R., Sohn D., Tokumine S., Valter D., Vasudevan V., Vodrahalli K., Wang X., Wang P., Wang Z., Wang T., Wieting J., Wu Y., Xu K., Xu Y., Xue L., Yin P., Yu J., Zhang Q., Zheng S., Zheng C., Zhou W., Zhou D., Petrov S., Wu Y. (2023). Llama 2: Open Foundation and Fine-Tuned Chat Models // https://arxiv.org/abs/2307.09288

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Mistral AI team (2023). Mistral 7B. The best 7B model to date, Apache 2.0 // mistral.ai, September 27, 2023 // https://mistral.ai/news/announcing-mistral-7b/

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Elsen E., Odena A., Nye M., Taşırlar S., Dao T., Hawthorne C., Moparthi D., Somani A. (2023). Releasing Persimmon-8B / Adept, September 7, 2023 // https://www.adept.ai/blog/persimmon-8b

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Li Y., Bubeck S., Eldan R., Giorno A. D., Gunasekar S., Lee Y. T. (2023). Textbooks Are All You Need II: phi-1.5 technical report // https://arxiv.org/abs/2309.05463

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Dai X., Hou J., Ma C., Tsai S., Wang J., Wang R., Zhang P., Vandenhende S., Wang X., Dubey A., Yu M., Kadian A., Radenovic F., Mahajan D., Li K., Zhao Y., Petrovic V., Singh M. K., Motwani S., Wen Y., Song Y., Sumbaly R., Ramanathan V., He Z., Vajda P., Parikh D. (2023). Emu: Enhancing Image Generation Models Using Photogenic Needles in a Haystack // https://arxiv.org/abs/2309.15807

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Soboleva D., Al-Khateeb F., Myers R., Steeves J. R., Hestness J., Nolan D. (2023). SlimPajama: A 627B token cleaned and deduplicated version of RedPajama // https://www.cerebras.net/blog/slimpajama-a-627b-token-cleaned-and-deduplicated-version-of-redpajama

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2676

^* ^* ^* В настоящее время исследователи активно изучают и другие формы обучения с подкреплением для языковых моделей, например прямую оптимизацию политики (Direct Policy Optimization, DPO) и даже обучение с обратной связью от ИИ (RL from AI Feedback, RLAIF).

2677

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Bommasani R., Hudson D. A, Adeli E., Altman R., Arora S., von Arx S., Bernstein M. S., Bohg J., Bosselut A., Brunskill E., Brynjolfsson E., Buch S., Card D., Castellon R., Chatterji N., Chen A., Creel K., David J. Q., Demszky D., Donahue C., Doumbouya M., Durmus E., Ermon S., Etchemendy J., Ethayarajh K., Fei-Fei L., Finn C., Gale T., Gillespie L., Goel K., Goodman N., Grossman S., Guha N., Hashimoto T., Henderson P., Hewitt J., Ho D. E., Hong J., Hsu K., Huang J., Icard T., Jain S., Jurafsky D., Kalluri P., Karamcheti S., Keeling G., Khani F., Khattab O., Koh P. W., Krass M., Krishna R., Kuditipudi R., Kumar A., Ladhak F., Lee M., Lee T., Leskovec J., Levent I., Li X. L., Li X., Ma T., Malik A., Manning C. D., Mirchandani S., Mitchell E., Munyikwa Z., Nair S., Narayan A., Narayanan D., Newman B., Nie A., Niebles J. C., Nilforoshan H., Nyarko J., Ogut G., Orr L., Papadimitriou I., Park J. S., Piech C., Portelance E., Potts C., Raghunathan A., Reich R., Ren H., Rong F., Roohani Y., Ruiz C., Ryan J., Ré C., Sadigh D., Sagawa S., Santhanam K., Shih A., Srinivasan K., Tamkin A., Taori R., Thomas A. W., Tramèr F., Wang R. E., Wang W., Wu B., Wu J., Wu Y., Xie S. M., Yasunaga M., You J., Zaharia M., Zhang M., Zhang T., Zhang X., Zhang Y. (2021). On the Opportunities and Risks of Foundation Models // https://arxiv.org/abs/2108.07258

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Dao T. (2023). FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning // https://arxiv.org/abs/2307.08691

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^* Серебряная пуля — метафора, означающая простое решение сложной проблемы.

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Tay Y., Dehghani M., Abnar S., Chung H. W., Fedus W., Rao J., Narang S., Tran V. Q., Yogatama D., Metzler D. (2022). Scaling Laws vs Model Architectures: How does Inductive Bias Influence Scaling? // https://arxiv.org/abs/2207.10551

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Wei J., Wang X., Schuurmans D., Bosma M., Ichter B., Xia F., Chi E., Le Q., Zhou D. (2022). Chain-of-Thought Prompting Elicits Reasoning in Large Language Models // https://arxiv.org/abs/2201.11903

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Yao S., Yu D., Zhao J., Shafran I., Griffiths T. L., Cao Y., Narasimhan K. (2023). Tree of Thoughts: Deliberate Problem Solving with Large Language Models // https://arxiv.org/abs/2305.10601

2701

Besta M., Blach N., Kubicek A., Gerstenberger R., Gianinazzi L., Gajda J., Lehmann T., Podstawski M., Niewiadomski H., Nyczyk P., Hoefler T. (2023). Graph of Thoughts: Solving Elaborate Problems with Large Language Models // https://arxiv.org/abs/2308.09687

2702

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2703

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Dai Z., Yang Z., Yang Y., Carbonell J., Le Q. V., Salakhutdinov R. (2019). Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context // https://arxiv.org/abs/1901.02860

2705

Giannou A., Rajput S., Sohn J.-Y., Lee K., Lee J. D., Papailiopoulos D. (2023). Looped Transformers as Programmable Computers // https://arxiv.org/abs/2301.13196

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Fojo D., Campos V., Giro-i-Nieto X. (2018). Comparing Fixed and Adaptive Computation Time for Recurrent Neural Networks // https://arxiv.org/abs/1803.08165

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2710

Peng B., Alcaide E., Anthony Q., Albalak A., Arcadinho S., Cao H., Cheng X., Chung M., Grella M., GV K. K., He X., Hou H., Kazienko P., Kocon J., Kong J., Koptyra B., Lau H., Mantri K. S. I., Mom F., Saito A., Tang X., Wang B., Wind J. S., Wozniak S., Zhang R., Zhang Z., Zhao Q., Zhou P., Zhu J., Zhu R. (2023). Reinventing RNNs for the Transformer Era // https://arxiv.org/abs/2305.13048

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Fu D. Y., Dao T., Saab K. K., Thomas A. W., Rudra A., Ré C. (2022). Hungry Hungry Hippos: Towards Language Modeling with State Space Models // https://arxiv.org/abs/2212.14052

2712

Gu A., Goel K., Ré C. (2021). Efficiently Modeling Long Sequences with Structured State Spaces // Статья: https://arxiv.org/abs/2111.00396

2713

Gu A., Johnson I., Timalsina A., Rudra A., Ré C. (2022). How to Train Your HiPPO: State Space Models with Generalized Orthogonal Basis Projections // https://arxiv.org/abs/2206.12037

2714

Hasani R., Lechner M., Wang T.-H., Chahine M., Amini A., Rus D. (2022). Liquid Structural State-Space Models // https://arxiv.org/abs/2209.12951

2715

Gu A., Gupta A., Goel K., Ré C. (2022). On the Parameterization and Initialization of Diagonal State Space Models // https://arxiv.org/abs/2206.11893

2716

Smith J. T. H., Warrington A., Linderman S. W. (2022). Simplified State Space Layers for Sequence Modeling // https://arxiv.org/abs/2208.04933

2717

Sun Y., Dong L., Huang S., Ma S., Xia Y., Xue J., Wang J., Wei F. (2023). Retentive Network: A Successor to Transformer for Large Language Models // https://arxiv.org/abs/2307.08621

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Hao S., Liu T., Wang Z., Hu Z. (2023). ToolkenGPT: Augmenting Frozen Language Models with Massive Tools via Tool Embeddings // https://arxiv.org/abs/2305.11554

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2722

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2724

^* Сегодня для такого синтеза часто используют термин «генерация, дополненная поиском» (Retrieval-augmented Generation, RAG).

2725

Schlag I., Sukhbaatar S., Celikyilmaz A., Yih W.-t., Weston J., Schmidhuber J., Li X. (2023). Large Language Model Programs // https://arxiv.org/abs/2305.05364

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Khattab O., Santhanam K., Li X. L., Hall D., Liang P., Potts C., Zaharia M. (2022). Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP // https://arxiv.org/abs/2212.14024

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Zhuge M., Liu H., Faccio F., Ashley D. R., Csordás R., Gopalakrishnan A., Hamdi A., Hammoud H. A. A. K., Herrmann V., Irie K., Kirsch L., Li B., Li G., Liu S., Mai J., Piękos P., Ramesh A., Schlag I., Shi W., Stanić A., Wang W., Wang Y., Xu M., Fan D.-P., Ghanem B., Schmidhuber J. (2023). Mindstorms in Natural Language-Based Societies of Mind // https://arxiv.org/abs/2305.17066

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Panzarino M. (2014). Yahoo Wins Another Apple Design Award For News Digest App / TechCrunch, June 3, 2014. // https://techcrunch.com/2014/06/02/yahoo-wins-another-apple-design-award-for-news-digest-app/

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Gong S., Sinnott R. O., Qi J., Paris C. (2023). Fake News Detection Through Graph-based Neural Networks: A Survey // https://arxiv.org/abs/2307.12639

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Hariharan B., Arbeláez P., Girshick R., Malik J. (2015). Hypercolumns for Object Segmentation and Fine-grained Localization // https://arxiv.org/abs/1411.5752

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Dahl R. (2016). Google Brain Residency // https://tinyclouds.org/residency/

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Zhang R., Zhu J.-Y., Isola P., Geng X., Lin A. S., Yu T., Efros A. A. (2017). Real-Time User-Guided Image Colorization with Learned Deep Priors // https://arxiv.org/abs/1705.02999

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Colorization (2022) // https://paperswithcode.com/task/colorization/latest, https://paperswithcode.com/task/colorization/codeless#code

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White A. (2015). The Worst Time Of Year For The Most Patient And Polite Man On The Internet Has Begun. We should probably honour him with a statue or something / BuzzFeed, Nov 6, 2015 // https://www.buzzfeed.com/alanwhite/whats-the-definition-of-madness-again

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Sanusi V. (2016). The Most Patient And Polite Man On The Internet Is Back At It Again / BuzzFeed, Nov 10, 2016 // https://www.buzzfeed.com/victoriasanusi/its-the-worst-time-of-year-for-the-most-patient-and-polite-m

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Massey N. (2014). Man called John Lewis sent Christmas advert penguin after being bombarded with tweets directed at store / Mirror, 20 Nov 2014 // https://www.mirror.co.uk/news/uk-news/man-called-john-lewis-sent-4658776

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Vincent J. (2016). This AI program sees genitals everywhere it looks. Do androids dream of electric dicks? / The Verge, Oct 24, 2016 // https://www.theverge.com/2016/10/24/13379208/ai-nsfw-neural-nets-deep-dream-genitals

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Gatys L. A., Ecker A. S., Bethge M. (2015). A Neural Algorithm of Artistic Style // https://arxiv.org/abs/1508.06576

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Salimans T., Goodfellow I., Zaremba W., Cheung V., Radford A., Chen X. (2016). Improved Techniques for Training GANs // https://arxiv.org/abs/1606.03498

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Goodfellow I. J., Pouget-Abadie J., Mirza M., Xu B., Warde-Farley D., Ozair S., Courville A., Bengio Y. (2014). GenerativeAdversarialNetworks // https://arxiv.org/abs/1406.2661

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Alberge D. (2021). Was famed Samson and Delilah really painted by Rubens? No, says AI / The Guardian, 26 Sep 2021 // https://www.theguardian.com/artanddesign/2021/sep/26/was-famed-samson-and-delilah-really-painted-by-rubens-no-says-ai

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Schmidhuber J. (1992). Learning factorial codes by predictability minimization / Neural Computation, Vol. 4 (6), pp. 863—879 // https://doi.org/10.1162/neco.1992.4.6.863

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Mirza M., Osindero S. (2014). Conditional Generative Adversarial Nets // https://arxiv.org/abs/1411.1784

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Isola P., Zhu J.-Y., Zhou T., Efros A. A. (2016). Image-to-Image Translation with Conditional Adversarial Networks // https://arxiv.org/abs/1611.07004

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Zhu J.-Y., Park T., Isola P., Efros A. A. (2017). Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks // https://arxiv.org/abs/1703.10593

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Shrivastava A., Pfister T., Tuzel O., Susskind J., Wang W., Webb R. (2016). Learning from Simulated and Unsupervised Images through Adversarial Training // https://arxiv.org/abs/1612.07828

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Isola P., Zhu J.-Y., Zhou T., Efros A. A. (2016). Image-to-Image Translation with Conditional Adversarial Networks // https://arxiv.org/abs/1611.07004

2770

Choi Y., Choi M., Kim M., Ha J.-W., Kim S., Choo J. (2017). StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation // https://arxiv.org/abs/1711.09020

2771

Iizuka S., Simo-Serra E., Ishikawa H. (2017). Globally and Locally Consistent Image Completion / ACM Transactions on Graphics, Vol. 36, Iss. 4, Article 107, July 2017 // http://dx.doi.org/10.1145/3072959.3073659

2772

Sagong M.-C., Shin Y.-G., Kim S.-W., Park S., Ko S.-J. (2019). PEPSI: Fast Image Inpainting With Parallel Decoding Network / 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) // https://doi.org/10.1109/CVPR.2019.01162

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Shin Y.-G., Sagong M.-C., Yeo Y.-J., Kim S.-W., Ko S.-J. (2019). PEPSI++: Fast and Lightweight Network for Image Inpainting // https://arxiv.org/abs/1905.09010

2774

DeepCreamPy: Decensoring Hentai with Deep Neural Networks // https://github.com/deeppomf/DeepCreamPy

2775

Radford A., Metz L., Chintala S. (2015). Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks // https://arxiv.org/abs/1511.06434

2776

Chen X., Duan Y., Houthooft R., Schulman J., Sutskever I., Abbeel P. (2016). InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets // https://arxiv.org/abs/1606.03657

2777

Kim T., Cha M., Kim H., Lee J. K., Kim J. (2017). Learning to Discover Cross-Domain Relations with Generative Adversarial Networks // https://arxiv.org/abs/1703.05192

2778

Karras T., Aila T., Laine S., Lehtinen J. (2017). Progressive Growing of GANs for Improved Quality, Stability, and Variation // https://arxiv.org/abs/1710.10196

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Arjovsky M., Chintala S., Bottou L. (2017). Wasserstein GAN // https://arxiv.org/abs/1701.07875

2780

Gulrajani I., Ahmed F., Arjovsky M., Dumoulin V., Courville A. (2017). Improved Training of Wasserstein GANs // https://arxiv.org/abs/1704.00028

2781

Karras T., Laine S., Aila T. (2018). A Style-Based Generator Architecture for Generative Adversarial Networks // https://arxiv.org/abs/1812.04948

2782

Karras T., Laine S., Aittala M., Hellsten J., Lehtinen J., Aila T. (2019). Analyzing and Improving the Image Quality of StyleGAN // https://arxiv.org/abs/1912.04958

2783

Karras T., Aittala M., Laine S., Härkönen E., Hellsten J., Lehtinen J., Aila T. (2021). Alias-Free Generative Adversarial Networks // https://arxiv.org/abs/2106.12423

2784

Choi Y., Uh Y., Yoo J., Ha J.-W. (2019). StarGAN v2: Diverse Image Synthesis for Multiple Domains // https://arxiv.org/abs/1912.01865

2785

Mokady R., Yarom M., Tov O., Lang O., Cohen-Or D., Dekel T., Irani M., Mosseri I. (2022). Self-Distilled StyleGAN: Towards Generation from Internet Photos // https://arxiv.org/abs/2202.12211

2786

Stanford Human-Centered Artificial Intelligence (HAI) (2021). Artificial Intelligence Index Report 2021 // https://aiindex.stanford.edu/wp-content/uploads/2021/03/2021-AI-Index-Report_Master.pdf

2787

Akbari H., Yuan L., Qian R., Chuang W.-H., Chang S.-F., Cui Y., Gong B. (2021). VATT: Transformers for Multimodal Self-Supervised Learning from Raw Video, Audio and Text // https://arxiv.org/abs/2104.11178

2788

Baevski A., Hsu W.-N., Xu Q., Babu A., Gu J., Auli M. (2022). The first high-performance self-supervised algorithm that works for speech, vision, and text / Meta AI, January 20, 2022

2789

Mitrovic J., McWilliams B., Walker J., Buesing L., Blundell C. (2020). Representation Learning via Invariant Causal Mechanisms // https://arxiv.org/abs/2010.07922

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Tomasev N., Bica I., McWilliams B., Buesing L., Pascanu R., Blundell C., Mitrovic J. (2022). Pushing the limits of self-supervised ResNets: Can we outperform supervised learning without labels on ImageNet? // https://arxiv.org/abs/2201.05119

2791

^* В машинном обучении авторегрессионными обычно называют модели для предсказания следующего элемента последовательности на основе предыдущих её элементов.

2792

van den Oord A., Kalchbrenner N., Kavukcuoglu K. (2016). Pixel Recurrent Neural Networks // https://arxiv.org/abs/1601.06759

2793

van den Oord A., Kalchbrenner N., Vinyals O., Espeholt L., Graves A., Kavukcuoglu K. (2016). Conditional Image Generation with PixelCNN Decoders // https://arxiv.org/abs/1606.05328

2794

Salimans T., Karpathy A., Chen X., Kingma D. P. (2017). PixelCNN++: Improving the PixelCNN with Discretized Logistic Mixture Likelihood and Other Modifications // https://arxiv.org/abs/1701.05517

2795

Sohl-Dickstein J., Weiss E. A., Maheswaranathan N., Ganguli S. (2015). Deep Unsupervised Learning using Nonequilibrium Thermodynamics // https://arxiv.org/abs/1503.03585

2796

Ho J., Jain A., Abbeel P. (2020). Denoising Diffusion Probabilistic Models // https://arxiv.org/abs/2006.11239

2797

Nichol A., Dhariwal P. (2021). Improved denoising diffusion probabilistic models // https://arxiv.org/abs/2102.09672

2798

Dhariwal P., Nichol A. (2021). Diffusion Models Beat GANs on Image Synthesis // https://arxiv.org/abs/2105.05233

2799

Jiang Y., Chang S., Wang Z. (2021). TransGAN: Two Pure Transformers Can Make One Strong GAN, and That Can Scale Up // https://arxiv.org/abs/2102.07074

2800

Zhang H., Xu T., Li H., Zhang S., Wang X., Huang X., Metaxas D. (2018). StackGAN++: Realistic Image Synthesis with Stacked Generative Adversarial Networks // https://arxiv.org/abs/1710.10916

2801

Wah C., Branson S., Welinder P., Perona P., Belongie S. (2011). The Caltech-UCSD Birds-200-2011 Dataset. Technical Report CNS-TR2011-001, California Institute of Technology // http://www.vision.caltech.edu/visipedia/papers/CUB_200_2011.pdf

2802

Zhang H., Xu T., Li H., Zhang S., Wang X., Huang X., Metaxas D. (2017). StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks // https://arxiv.org/abs/1710.10916

2803

Sun W., Chen Z. (2019). Learned Image Downscaling for Upscaling using Content Adaptive Resampler // https://arxiv.org/abs/1907.12904

2804

Lim B., Son S., Kim H., Nah S., Lee K. M. (2017). Enhanced Deep Residual Networks for Single Image Super-Resolution // https://arxiv.org/abs/1707.02921

2805

Ma C., Rao Y., Cheng Y., Chen C., Lu J., Zhou J. (2020). Structure-Preserving Super Resolution with Gradient Guidance // https://arxiv.org/abs/2003.13081

2806

Niu B., Wen W., Ren W., Zhang X., Yang L., Wang S., Zhang K., Cao X., Shen H. (2020). Single Image Super-Resolution via a Holistic Attention Network // https://arxiv.org/abs/2008.08767

2807

Kawulok M., Benecki P., Piechaczek S., Hrynczenko K., Kostrzewa D., Nalepa J. (2019). Deep Learning for Multiple-Image Super-Resolution // https://arxiv.org/abs/1903.00440

2808

Zhu M., Pan P., Chen W., Yang Y. (2019). DM-GAN: Dynamic Memory Generative Adversarial Networks for Text-to-Image Synthesis // https://arxiv.org/abs/1904.01310

2809

Xu T., Zhang P., Huang Q., Zhang H., Gan Z., Huang X., He X. (2017). AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks // https://arxiv.org/abs/1711.10485

2810

Liang J., Pei W., Lu F. (2019). CPGAN: Full-Spectrum Content-Parsing Generative Adversarial Networks for Text-to-Image Synthesis // https://paperswithcode.com/paper/cpgan-full-spectrum-content-parsing

2811

Parmar N., Vaswani A., Uszkoreit J., Kaiser Ł., Shazeer N., Ku A., Tran D. (2018). Image Transformer // https://arxiv.org/abs/1802.05751

2812

Wu B., Xu C., Dai X., Wan A., Zhang P., Yan Z., Tomizuka M., Gonzalez J., Keutzer K., Vajda P. (2020). Visual Transformers: Token-based Image Representation and Processing for Computer Vision // https://arxiv.org/abs/2006.03677

2813

Dosovitskiy A., Beyer L., Kolesnikov A., Weissenborn D., Zhai X., Unterthiner T., Dehghani M., Minderer M., Heigold G., Gelly S., Uszkoreit J., Houlsby N. (2020). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale // https://arxiv.org/abs/2010.11929

2814

Touvron H., Cord M., Douze M., Massa F., Sablayrolles A., Jégou H. (2020). Training data-efficient image transformers & distillation through attention // https://arxiv.org/abs/2012.12877

2815

Liu Z., Lin Y., Cao Y., Hu H., Wei Y., Zhang Z., Lin S., Guo B. (2021). Swin Transformer: Hierarchical Vision Transformer using Shifted Windows // https://arxiv.org/abs/2103.14030

2816

Carion N., Massa F., Synnaeve G., Usunier N., Kirillov A., Zagoruyko S. (2020). End-to-end Object Detection with Transformers // https://ai.facebook.com/research/publications/end-to-end-object-detection-with-transformers

2817

Zhu X., Su W., Lu L., Li B., Wang X., Dai J. (2020). Deformable DETR: Deformable Transformers for End-to-End Object Detection // https://arxiv.org/abs/2010.04159

2818

Guo J., Han K., Wu H., Xu C., Tang Y., Xu C., Wang Y. (2021). CMT: Convolutional Neural Networks Meet Vision Transformers // https://arxiv.org/abs/2107.06263

2819

Wu H., Xiao B., Codella N., Liu M., Dai X., Yuan L., Zhang L. (2021). CvT: Introducing Convolutions to Vision Transformers // https://arxiv.org/abs/2103.15808

2820

Touvron H., Cord M., Sablayrolles A., Synnaeve G., Jégou H. (2021). Going deeper with Image Transformers // https://arxiv.org/abs/2103.17239

2821

Yuan K., Guo S., Liu Z., Zhou A., Yu F., Wu W. (2021). Incorporating Convolution Designs into Visual Transformers // https://arxiv.org/abs/2103.11816

2822

Chen M., Peng H., Fu J., Ling H. (2021). AutoFormer: Searching Transformers for Visual Recognition // https://arxiv.org/abs/2107.00651

2823

Han K., Xiao A., Wu E., Guo J., Xu C., Wang Y. (2021). Transformer in Transformer // https://arxiv.org/abs/2103.00112

2824

Wang Y., Huang R., Song S., Huang Z., Huang G. (2021). Not All Images are Worth 16x16 Words: Dynamic Transformers for Efficient Image Recognition // https://arxiv.org/abs/2105.15075

2825

Chen X., Hsieh C.-J., Gong B. (2021). When Vision Transformers Outperform ResNets without Pre-training or Strong Data Augmentations // https://arxiv.org/abs/2106.01548

2826

Dai Z., Liu H., Le Q. V., Tan M. (2021). CoAtNet: Marrying Convolution and Attention for All Data Sizes // https://arxiv.org/abs/2106.04803

2827

Liu Z., Hu H., Lin Y., Yao Z., Xie Z., Wei Y., Ning J., Cao Y., Zhang Z., Dong L., Wei F., Guo B. (2021). Swin Transformer V2: Scaling Up Capacity and Resolution // https://arxiv.org/abs/2111.09883

2828

Li Y., Wu C.-Y., Fan H., Mangalam K., Xiong B., Malik J., Feichtenhofer C. (2021). Improved Multiscale Vision Transformers for Classification and Detection // https://arxiv.org/abs/2112.01526

2829

Dong X., Bao J., Zhang T., Chen D., Zhang W., Yuan L., Chen D., Wen F., Yu N. (2021). PeCo: Perceptual Codebook for BERT Pre-training of Vision Transformers // https://arxiv.org/abs/2111.12710

2830

Wu S., Wu T., Tan H., Guo G. (2021). Pale Transformer: A General Vision Transformer Backbone with Pale-Shaped Attention // https://arxiv.org/abs/2112.14000

2831

Liu Z., Mao H., Wu C.-Y., Feichtenhofer C., Darrell T., Xie S. (2022). A ConvNet for the 2020s // https://arxiv.org/abs/2201.03545

2832

Chen X., Liang C., Huang D., Real E., Wang K., Liu Y., Pham H., Dong X., Luong T., Hsieh C.-J., Lu Y., Le Q. V. (2023). BASIC-L: Symbolic Discovery of Optimization Algorithms // https://arxiv.org/abs/2302.06675

2833

CoCa: Chen X., Liang C., Huang D., Real E., Wang K., Liu Y., Pham H., Dong X., Luong T., Hsieh C.-J., Lu Y., Le Q. V. (2022). Symbolic Discovery of Optimization Algorithms // https://arxiv.org/abs/2302.06675

2834

Ramesh A., Pavlov M., Goh G., Gray S., Chen M., Child R., Misra V., Mishkin P, Krueger G., Agarwal S., Sutskever I. (2021). DALL·E: Creating Images from Text / OpenAI Blog, January 5, 2021 // https://openai.com/blog/dall-e/

2835

Radford A., Sutskever I., Kim J. W., Krueger G., Agarwal S. (2021). CLIP: Connecting Text and Images / OpenAI Blog, January 5, 2021 // https://openai.com/blog/clip/

2836

Radford A., Sutskever I., Kim J. W., Krueger G., Agarwal S. (2021). CLIP: Connecting Text and Images / OpenAI Blog, January 5, 2021 // https://openai.com/blog/clip/

2837

Radford A., Kim J. W., Hallacy C., Ramesh A., Goh G., Agarwal S., Sastry G., Askell A., Mishkin P., Clark J., Krueger G., Sutskever I. (2021). Learning Transferable Visual Models From Natural Language Supervision // https://arxiv.org/abs/2103.00020

2838

Schuhmann C., Beaumont R., Vencu R., Gordon C., Wightman R., Cherti M., Coombes T., Katta A., Mullis C., Wortsman M., Schramowski P., Kundurthy S., Crowson K., Schmidt L., Kaczmarczyk R., Jitsev J. (2022). LAION-5B: An open large-scale dataset for training next generation image-text models // https://arxiv.org/abs/2210.08402

2839

Schuhmann C., Vencu R., Beaumont R., Kaczmarczyk R., Mullis C., Katta A., Coombes T., Jitsev J., Komatsuzaki A. (2021). LAION-400M: Open Dataset of CLIP-Filtered 400 Million Image-Text Pairs // https://arxiv.org/abs/2111.02114

2840

Ramesh A., Pavlov M., Goh G., Gray S., Voss C., Radford A., Chen M., Sutskever I. (2021). Zero-Shot Text-to-Image Generation // https://arxiv.org/abs/2102.12092

2841

https://github.com/sberbank-ai/sber-vq-gan

2842

Wang X., Yu K., Wu S., Gu J., Liu Y., Dong C., Loy C. C., Qiao Y., Tang X. (2018). ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks // https://arxiv.org/abs/1809.00219

2843

Сбер создал первую мультимодальную нейросеть ruDALL-E, которая генерирует картинки по описанию на русском языке (2021) / Sber Press, 2 ноября 2021 // https://press.sber.ru/publications/sber-sozdal-pervuiu-multimodalnuiu-neiroset-rudall-e-kotoraia-generiruet-kartinki-po-opisaniiu-na-russkom-iazyke

2844

Димитров Д. (2021). ruDALL-E: генерируем изображения по текстовому описанию, или Самый большой вычислительный проект в России / Хабр, 2 ноября // https://habr.com/ru/company/sberbank/blog/586926/

2845

https://github.com/sberbank-ai/ru-dalle

2846

Nichol A., Dhariwal P., Ramesh A., Shyam P., Mishkin P., McGrew B., Sutskever I., Chen M. (2021). GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models // https://arxiv.org/abs/2112.10741

2847

Gupta T., Kamath A., Kembhavi A., Hoiem D. (2021). Towards General Purpose Vision Systems // https://arxiv.org/abs/2104.00743

2848

^* Гипермодальность — свойство мультимодальной модели, позволяющее ей использовать как на входе, так и на выходе данные, представленные любым подмножеством поддерживаемых модальностей, а не только какой-либо одной. В случае ruDOLPH это означает, что как на входе, так и на выходе модели могут быть либо только текст, либо только изображение, либо последовательности вида «изображение — текст» или «текст — изображение».

2849

Shonenkov A., Konstantinov M. (2021). RuDOLPH: One Hyper-Modal Transformer can be creative as DALL-E and smart as CLIP // https://github.com/sberbank-ai/ru-dolph

2850

Ramesh A., Dhariwal P., Nichol A., Chu C., Chen M. (2022). Hierarchical Text-Conditional Image Generation with CLIP Latents // https://arxiv.org/pdf/2204.06125.pdf

2851

Daras G., Dimakis A. G. (2022). Discovering the Hidden Vocabulary of DALLE-2 // https://arxiv.org/abs/2206.00169

2852

^* Blackbox-методы или методы «чёрного ящика» — обобщённое название методов, которые анализируют тот или иной объект лишь через взаимодействие с ним, не заглядывая в его внутреннее устройство.

2853

Костенков А. (2022). Нейросеть DALL-E 2 создала собственный язык: правда, не совсем, и совсем не? / Habr, 18 июня 2022 // https://habr.com/ru/companies/ruvds/articles/672046/

2854

Daras G. (2022). / Twitter, 31 мая 2022 // https://twitter.com/giannis_daras/status/1531693093040230402

2855

Quach K. (2022). No, OpenAI's image-making DALL·E 2 doesn't understand some secret language / The Register, 7 Jun 2022 // https://www.theregister.com/2022/06/07/in_brief_ai/

2856

Bach J. (2022). / Twitter, 31 мая 2022 // https://twitter.com/Plinz/status/1531711345585860609

2857

^* Создатели моделей для генерации изображений стремятся улучшить эту ситуацию: например, запущенный в августе 2023 г. сервис Ideogram способен справиться с визуализацией небольших предложений. В основе сервиса лежит диффузионная генеративная модель, в создании которой принимали участие разработчики нейросети Imagen. Появившаяся в октябре 2023 г. DALL·E 3 также продемонстрировала весьма значительный прогресс в задаче визуализации текстов.

2858

Norouzi M., Chan W., Ho J., Saharia C., Abdullah S., Lei J., Lu J. (2023). Announcing Ideogram AI // https://ideogram.ai/launch

2859

Rombach R., Blattmann A., Lorenz D., Esser P., Ommer B. (2021). High-Resolution Image Synthesis with Latent Diffusion Models // https://arxiv.org/abs/2112.10752

2860

Quach K. (2022). No, OpenAI's image-making DALL·E 2 doesn't understand some secret language / The Register, 7 Jun 2022 // https://www.theregister.com/2022/06/07/in_brief_ai/

2861

OpenAI (2023). DALL·E 3 system card // https://openai.com/research/dall-e-3-system-card

2862

Saharia C., Chan W., Saxena S., Li L., Whang J., Denton E., Ghasemipour S. K. S., Ayan B. K., Mahdavi S. S., Lopes R. G., Salimans T., Ho J., Fleet D. J., Norouzi N. (2022). Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding // https://arxiv.org/abs/2205.11487

2863

Midjourney LLC (2022). Midjourney Documentation // https://docs.midjourney.com/v1/en

2864

Vincent J. (2022). ‘An engine for the imagination’: the rise of AI image generators. An interview with Midjourney founder David Holz. / The Verge, Aug 2, 2022 // https://www.theverge.com/2022/8/2/23287173/ai-image-generation-art-midjourney-multiverse-interview-david-holz

2865

Gu J., Zhai S., Zhang Y., Susskind J., Jaitly N. (2023). Matryoshka Diffusion Models // https://arxiv.org/abs/2310.15111

2866

Shonenkov A., Konstantinov M., Bakshandaeva D., Schuhmann C., Ivanova K., Klokova N. (2023). IF by DeepFloyd Lab at StabilityAI // https://github.com/deep-floyd/IF

2867

Разжигаев А. (2022). Kandinsky 2.0 — первая мультиязычная диффузия для генерации изображений по тексту. / Habr, 23 ноя 2022 // https://habr.com/ru/companies/sberbank/articles/701162/

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Razzhigaev A., Shakhmatov A., Maltseva A., Arkhipkin V., Pavlov I., Ryabov I., Kuts A., Panchenko A., Kuznetsov A., Dimitrov D. (2023). Kandinsky: an Improved Text-to-Image Synthesis with Image Prior and Latent Diffusion // https://arxiv.org/abs/2310.03502

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Кузнецов А. (2022). Kandinsky 2.1, или Когда +0,1 значит очень много. / Habr, 4 апр 2023 // https://habr.com/ru/companies/sberbank/articles/725282/

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Димитров Д. (2023). Kandinsky 2.2 — новый шаг в направлении фотореализма / Habr, 12 июля 2023. // https://habr.com/ru/companies/sberbank/articles/747446/

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Valyaeva A. (2023). AI Has Already Created As Many Images As Photographers Have Taken in 150 Years. Statistics for 2023 / Everypixel Journal, 15.08.2023 // https://journal.everypixel.com/ai-image-statistics

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Zhang L., Agrawala M. (2023). Adding Conditional Control to Text-to-Image Diffusion Models // https://arxiv.org/abs/2302.05543

2873

Adobe (2023). Generative Fill // https://www.adobe.com/products/photoshop/generative-fill.html

2874

Marcus G. (2022). Horse rides astronaut / The Road to AI We Can Trust, 28.05.2022 // https://garymarcus.substack.com/p/horse-rides-astronaut

2875

Marcus G. (2022). Compositionality and Natural Language Understanding [slides] / The Challenge of Compositionality for AI / June 29-30, 2022 // https://compositionalintelligence.github.io/pdfs/Marcus.pdf

2876

^* Промпт-инженер — специалист по составлению запросов (затравок, промптов) [prompts] для генеративных нейронных сетей; промпт-инжиниринг — дисциплина, занимающаяся вопросами сочинения или оптимизации промптов; по сути промпт-инжиниринг является развитием идеи «затравочного программирования», знакомого нам по цитировавшимся ранее высказываниям Андрея Карпатого и Гверна Бренуэна.

2877

McCammon J. (2023). Can a horse ride an astronaut? A taxonomy of antagonistic Midjourney prompts / 96 layers, 12 июня 2023 // https://www.96layers.ai/p/can-a-horse-ride-an-astronaut

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Lovering C., Pavlick E. (2023). Training Priors Predict Text-To-Image Model Performance // https://arxiv.org/abs/2306.01755

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Tsalicoglou C., Manhardt F., Tonioni A., Niemeyer M., Tombari F. (2023). TextMesh: Generation of Realistic 3D Meshes From Text Prompts // https://arxiv.org/abs/2304.12439

2880

Mildenhall B., Srinivasan P. P., Tancik M., Barron J. T., Ramamoorthi R., Ng R. (2020). NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis // https://arxiv.org/abs/2003.08934

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Niemeyer M., Barron J. T., Mildenhall B., Sajjadi M. S. M., Geiger A., Radwan N. (2023). RegNeRF: Regularizing Neural Radiance Fields for View Synthesis from Sparse Inputs // https://arxiv.org/abs/2112.00724

2882

Poole B., Jain A., Barron J. T., Mildenhall B. (2022). DreamFusion: Text-to-3D using 2D Diffusion // https://arxiv.org/abs/2209.14988

2883

Müller T., Evans A., Schied C., Keller A. (2022). Instant Neural Graphics Primitives with a Multiresolution Hash Encoding // https://arxiv.org/abs/2201.05989

2884

Ben Melech Stan G., Wofk D., Fox S., Redden A., Saxton W., Yu J., Aflalo E., Tseng S.-Y., Nonato F., Muller M., Lal V. (2023). LDM3D: Latent Diffusion Model for 3D // https://arxiv.org/abs/2305.10853

2885

Ranftl R., Lasinger K., Hafner D., Schindler K., Koltun V. (2019). Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer // https://arxiv.org/abs/1907.01341

2886

Deitke M., Liu R., Wallingford M., Ngo H., Michel O., Kusupati A., Fan A., Laforte C., Voleti V., Gadre S. Y., VanderBilt E., Kembhavi A., Vondrick C., Gkioxari G., Ehsani K., Schmidt L., Farhadi A. (2023). Objaverse-XL: A Universe of 10M+ 3D Objects // https://arxiv.org/abs/2307.05663

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Deitke M., Schwenk D., Salvador J., Weihs L., Michel O., VanderBilt E., Schmidt L., Ehsani K., Kembhavi A., Farhadi A. (2022). Objaverse: A Universe of Annotated 3D Objects // https://arxiv.org/abs/2212.08051

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Cheung R. (2023). Is the Panic Over AI Art Overblown? We Speak With Artists and Experts. / Vice, February 22, 2023 // https://www.vice.com/en/article/ake53e/ai-art-lawsuits-midjourney-dalle-chatgpt

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Yu J., Xu Y., Koh J. Y., Luong T., Baid G., Wang Z., Vasudevan V., Ku A., Yang Y., Ayan B. K., Hutchinson B., Han W., Parekh Z., Li X., Zhang H., Baldridge J., Wu Y. (2022). Scaling Autoregressive Models for Content-Rich Text-to-Image Generation // https://arxiv.org/abs/2206.10789

2890

Craiyon LLC (2023). Frequently asked questions // https://www.craiyon.com/#faq

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Yuan L., Chen D., Chen Y.-L., Codella N., Dai X., Gao J., Hu H., Huang X., Li B., Li C., Liu C., Liu M., Liu Z., Lu Y., Shi Y., Wang L., Wang J., Xiao B., Xiao Z., Yang J., Zeng M., Zhou L., Zhang P. (2021). Florence: A New Foundation Model for Computer Vision // https://arxiv.org/abs/2111.11432

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Wu C., Liang J., Ji L., Yang F., Fang Y., Jiang D., Duan N. (2021). NÜWA: Visual Synthesis Pre-training for Neural visUal World creAtion // https://arxiv.org/abs/2111.12417

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Jia C., Yang Y., Xia Y., Chen Y.-T., Parekh Z., Pham H., Le Q. V., Sung Y., Li Z., Duerig T. (2021). Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision // https://arxiv.org/abs/2102.05918

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Riquelme C., Puigcerver J., Mustafa B., Neumann M., Jenatton R., Pinto A. S., Keysers D., Houlsby N. (2021). Scaling Vision with Sparse Mixture of Experts // https://arxiv.org/abs/2106.05974

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Alayrac J.-B., Donahue J., Luc P., Miech A., Barr I., Hasson Y., Lenc K., Mensch A., Millican K., Reynolds M., Ring R., Rutherford E., Cabi S., Han T., Gong Z., Samangooei S., Monteiro M., Menick J., Borgeaud S., Brock A., Nematzadeh A., Sharifzadeh S., Binkowski M., Barreira R., Vinyals O., Zisserman A., Simonyan K. (2022). Flamingo: a Visual Language Model for Few-Shot Learning // https://storage.googleapis.com/deepmind-media/DeepMind.com/Blog/tackling-multiple-tasks-with-a-single-visual-language-model/flamingo.pdf

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Cizek K., Uricchio W., Wolozin S. (2019). Media co-creation with non-human systems / Cizek K., Uricchio W., Anderson J., Carter M. A., Detroit Narrative Agency, Harris T. A., Holmes M., Lachman R., Massiah L., Mertes C., Rafsky S., Stephenson M., Winger-Bearskin A., Wolozin S. (2019). Collective Wisdom. Massachusetts Institute of Technology // https://doi.org/10.21428/ba67f642.f7c1b7e5

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Radulovic P. (2018). Harrison Ford is the star of Solo: A Star Wars Story thanks to deepfake technology / Polygon, Oct 17, 2018 // https://www.polygon.com/2018/10/17/17989214/harrison-ford-solo-movie-deepfake-technology

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^* Здесь — полифонический приём преобразования нотной последовательности, заключающийся в воспроизведении её интервалов в противоположном направлении от некоего неизменяющегося звука: восходящему ходу в основном (прямом) движении партии в обратном движении соответствует ход на такой же интервал вниз, и наоборот.

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^* Думер (от англ. doom — злой рок, катастрофа, гибель) — человек, пессимистично смотрящий в будущее, считающий, что человечество по той или иной причине обречено; ИИ-думерами [AI doomers] иронично называют сторонников идеи о том, что развитие технологий ИИ неизбежно приведёт к гибели человечества или по крайней мере нанесёт ему тяжкий вред.

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^* Алгоритмическое общество — общество, организованное вокруг принятия социальных и экономических решений с помощью алгоритмов, роботов и агентов искусственного интеллекта.

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Holley P. (2019). Amazon's one-day delivery service depends on the work of thousands of robots / Washington Post, 06.07.2019 // https://www.washingtonpost.com/technology/2019/06/07/amazons-one-day-delivery-service-depends-work-thousands-robots

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Simon F. (2017). San Francisco Just Put the Brakes on Delivery Robots / Wired, 12.06.2017 // https://www.wired.com/story/san-francisco-just-put-the-brakes-on-delivery-robots/

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Vollset S. E., Goren E., Yuan C.-W., Cao J., Smith A. E., Hsiao T., Bisignano C., Azhar G. S., Castro E., Chalek J., Dolgert A. J., Frank T., Fukutaki K., Hay S. I, Lozano R., Mokdad A. H., Nandakumar V., Pierce M., Pletcher M., Robalik T., Steuben K. M., Wunrow H. Y., Zlavog B. S., Murray C. J. L. (2020). Fertility, mortality, migration, and population scenarios for 195 countries and territories from 2017 to 2100: a forecasting analysis for the Global Burden of Disease Study / Vol. 396, Iss. 10258, pp. 1285—1306 // https://doi.org/10.1016/S0140-6736(20)30677-2

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^* Пер. Л. Васильева и Н. Маркалова.

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^* Глубокий синтез [深度合成] — методика синтеза изображений, основанная на глубоких нейронных сетях, в просторечии — «дипфейк».

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For the first time in Israel: The principles of the policy for the responsible development of the field of artificial intelligence were published for public comment (2022). / Ministry of Innovation, Science and Technology, 17.11.2022 // https://www.gov.il/en/departments/news/most-news20221117

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^* Cambridge Analytica (CA) — британская частная компания, которая использовала продвинутые технологии анализа данных, собранных в социальных сетях, чтобы оказывать влияние на результаты выборов и референдумов.

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Chen L.-C., Collins M. D., Zhu Y., Papandreou G., Zoph B., Schroff F., Adam H., Shlens J. (2018). Searching for Efficient Multi-Scale Architectures for Dense Image Prediction // https://arxiv.org/abs/1809.04184

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Zhang Y., Zhang Q., Yang Y. (2020). How Does Supernet Help in Neural Architecture Search? // https://arxiv.org/abs/2010.08219

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3275

Wan A., Dai X., Zhang P., He Z., Tian Y., Xie S., Wu B., Yu M., Xu T., Chen K., Vajda P., Gonzalez J. E. (2020). FBNetV2: Differentiable Neural Architecture Search for Spatial and Channel Dimensions // https://arxiv.org/abs/2004.05565

3276

Awad N., Mallik N., Hutter F. (2020). Differential Evolution for Neural Architecture Search // https://arxiv.org/abs/2012.06400

3277

Jie R., Gao J. (2021). Differentiable Neural Architecture Search with Morphism-based Transformable Backbone Architectures // https://arxiv.org/abs/2106.07211

3278

Tian Y., Shen L., Shen L., Su G., Li Z., Liu W. (2020). AlphaGAN: Fully Differentiable Architecture Search for Generative Adversarial Networks // https://arxiv.org/abs/2006.09134

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Ding M., Lian X., Yang L., Wang P., Jin X., Lu Z., Luo P. (2021). HR-NAS: Searching Efficient High-Resolution Neural Architectures with Lightweight Transformers // https://arxiv.org/abs/2106.06560

3280

Yang Y., You S., Li H., Wang F., Qian C., Lin Z. (2021). Towards Improving the Consistency, Efficiency, and Flexibility of Differentiable Neural Architecture Search // https://arxiv.org/abs/2101.11342

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Jin H., Song Q., Hu X. (2018). Auto-Keras: An Efficient Neural Architecture Search System // https://arxiv.org/abs/1806.10282

3282

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Zela A., Siems J., Hutter F. (2020). NAS-Bench-1Shot1: Benchmarking and Dissecting One-shot Neural Architecture Search // https://arxiv.org/abs/2001.10422

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Dong X., Yang Y. (2020). NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search // https://arxiv.org/abs/2001.00326

3285

Tu R., Khodak M., Roberts N., Talwalkar A. (2021). NAS-Bench-360: Benchmarking Diverse Tasks for Neural Architecture Search // https://arxiv.org/abs/2110.05668

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Dong X., Liu L., Musial K., Gabrys B. (2020). NATS-Bench: Benchmarking NAS Algorithms for Architecture Topology and Size // https://arxiv.org/abs/2009.00437

3290

Klein A., Hutter F. (2019). Tabular Benchmarks for Joint Architecture and Hyperparameter Optimization // https://arxiv.org/abs/1905.04970

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Huang Y., Cheng Y., Bapna A., Firat O., Chen M. X., Chen D., Lee H. J., Ngiam J., Le Q. V., Wu Y., Chen Z. (2018). GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism // https://arxiv.org/abs/1811.06965

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Pham H., Dai Z., Xie Q., Luong M.-T., Le Q. V. (2020). Meta Pseudo Labels // https://arxiv.org/abs/2003.10580

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Zou A., Phan L., Chen S., Campbell J., Guo P., Ren R., Pan A., Yin X., Mazeika M., Dombrowski A.-K., Goel S., Li N., Byun M. J., Wang Z., Mallen A., Basart S., Koyejo S., Song D., Fredrikson M., Kolter J. Z., Hendrycks D. (2023). Representation Engineering: A Top-Down Approach to AI Transparency // https://arxiv.org/abs/2310.01405

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3332

^* Пер. С. Земляного.

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Tangermann V. (2023). Artists Sue Stable Diffusion and Midjourney for Using Their Work to Train AI That Steals Their Jobs / Futurism, Jan 18, 2023 // https://futurism.com/artists-sue-stabile-diffusion-midjourney

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Lee T. B. (2023). Stable Diffusion copyright lawsuits could be a legal earthquake for AI. / Ars Technica, 4/3/2023 // https://arstechnica.com/tech-policy/2023/04/stable-diffusion-copyright-lawsuits-could-be-a-legal-earthquake-for-ai/

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^* DeviantArt — популярный сервис обмена изображениями и социальная сеть; в конце 2022 г. DeviantArt выпустил собственный генератор изображений DreamUp, основанный на модели Stable Diffusion.

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