ௐেௌ௮ఐ৾ଢ଼஢ાଓஃস ৆ੁఐ৾੕়ଢ଼஢হ঵‫ق‬ಏ෫ଢ଼஢ಉICT੦ೕଡണ‫؞‬যੵੴચৰಎଢ଼஢হ঵‫ك‬৳௏ୢ௜৷যੵੴચभૼ୒୓ৗधব੠଼ଥৡ਱঱पৱघॊয౫୘ਛपঢ়घॊଢ଼஢‫ق‬H29-ICT-঳ಹ-004‫ك‬যੵੴચো୅যੵੴચभ঺ভৰಎभञीप૸୴প৾୰੹୰੹শ੫઺஼୰੹৽੾ଵ৶ଢ଼஢७থॱ‫્ ش‬ભ൥పนি ଝয

1. যੵੴચधम୦ऊ
2. যੵੴચ॑ણ৷घॊਔଝ
3. যੵੴચभഄఴधॺঞথॻ
4. ॹॕ‫ش‬উছ‫ॽش‬থॢभ৶૛৓ऩూବ
5. ୢ௜पउऐॊযੵੴચभણ৷୩ୠ
6. যੵੴચभ଒ੀधిજऩણ৷
7. যੵੴચ॑ણ৷खञଲષभ৹୸पणःथ

77

1. যੵੴચधम୦ऊ
2. যੵੴચ॑ણ৷घॊਔଝ
3. যੵੴચभഄఴधॺঞথॻ
4. ॹॕ‫ش‬উছ‫ॽش‬থॢभ৶૛৓ऩూବ
5. ୢ௜पउऐॊযੵੴચभણ৷୩ୠ
6. যੵੴચभ଒ੀधిજऩણ৷
7. যੵੴચ॑ણ৷खञଲષभ৹୸पणःथ

जुजुੴચधम‫ء‬঳ಹ৓पमੴ৓ऩણ৿भચৡ

ੴ৓ऩણ৿धम‫ء‬ਖ਻ੰৠ‫؞‬௓૛‫৾؞‬ಆऩनभੲਾ૪৶ચৡ
ྴ଴৲‫؞‬঳ಹ৲ऩन

যੵੴચधम‫ء‬঳ಹप‫ؚ‬َ୦ैऊभੴ৓৿੿ऋ૭ચऩੑ઴ਃ३५ॸ঒َُੴ৓৿੿ُधम‫ء‬॑ःअ‫؛‬৒ଝऋ૮ःযੵੴચदम‫ؚ‬ੴચभ३঑গঞ‫ش‬३ঙথघॊऒधपऩॊऋ‫ؚ‬ੴચभ३঑গঞ‫ش‬३ঙথऋੴચधःइॊऊ‫ء‬धःअઑਖ78ࠅ಼͹क͵‫ंڂݜ‬ͶΓΖਕ޽எ೵͹ఈٝ஦ౣऴ೯ޮཱིͺͫ͞ͱາཔ୉ָ ਕ޽దͶ͚ͯΔΗͪɼஎ೵Νࣰ࣍ͯସɽ͍Ζ͏ͺͨΗΝ͚ͯΘ͑ͳ෤ీӵ໎ࠅཱི৚ๅָ‫ॶڂݜ‬ͤΖ͞ͳͶΓͮͱஎ೵ࣙରΝ‫ͤڂݜ‬Ζ෾໼Ͳ͍Ζ੤ీ๝໎‫౐ښ‬୉ָʰஎ೵Ν࣍ͯϟΩʱ͵͏͢ͺʰৼΝ࣍ͯϟΩʱͲ͍Ζߖ޳ཀྵҲ࿢ਕ޽దͶ͚ͯͮͪஎద͵৾Ζ෥͏ΝͤΖͪΌ͹΍͹ʤεητϞʥͲ๼཰઎୼Ռָٗढ़୉ָӅ͍Ζௗඎਇ‫౐ښ‬୉ָਕؔ͹ಆ೶‫׈‬ಊΝ‫ݸۅ‬ΉͲεϝϣϪʖφͤΖεητϞͲ͍ΖຉߔҲ౨‫ښ‬୉ָਕ޽దͶࡠΖ৿͢͏எ೵͹੊ֆͲ͍Ζછీົ୉ࡗ୉ָஎ೵͹ఈٝ͗໎֮Ͳ͵͏͹Ͳɼਕ޽எ೵Ν໎֮ͶఈٝͲ͘͵͏ন‫ݬ‬ਖޮཱིͺͫ͞ͱາཔ୉ָ ‫ۅڂ‬Ͷͺਕؔͳۢพ͗෉͖͵͏ਕ޽ద͵எ೵͹͞ͳࢃ޳߶ฑ‫ܜ‬ጱٝग़୉ָ‫૳ݬ܂‬ు‫ـ‬௪৶୉ָࢃઔ޼‫ؽࢋܯ‬எ೵͹͑ͬͲɼਕؔ͗௜ંʀؔંͶઅ‫ͤܯ‬Ζ৖߻Νਕ޽எ೵χϭϱβਕ޽எ೵‫ॶڂݜ‬ͳ‫ݼ‬ΞͲྒྷ͏͹Ͳͺ͵͏͖ͳࢧ͑মഺदभ঵ੀभਸ਼঳য঻दुযੵੴચभ৒ଝम஘‫ر‬ࣙષͶΚΗΚΗ͗ϘρφΏਕͶંৰͤΖΓ͑͵ɼ৚ಊͳ৓஌Ͷຮͬஓ৏߶ࢦ दँॉ‫ଁؚ‬঳৓ऩ৒ଝऋਛऔोथःॊुभदमऩः౨‫ښ‬୉ָͪ૮‫༽ࡠޕ‬Νɼ෼ཀྵ๑ଉͶ‫͚͵ܐؖ‬ɼ͍Ζ͏ͺ‫ٱ‬Δͮͱɼਕ޽దͶ͚ͯΕड़ͦΖεητϞਕ͹எద͵৾Ζ෥͏Νໝแʀࢩԋʀ௔ӿͤΖͪΌ͹ߑ੔దεητϞਕ޽దͶ͚ͯΔΗΖஎ೵Ͳ͍Ζ͗ɼͨ͹எ೵͹ϪϗϩͺਕΝ௔͓ͱ͏Ζ΍͹Ν૟଀͢ͱ͏Ζਕ޽దͶ͚ͯΔΗͪਕؔ͹Γ͑͵எ೵ɼ͵͏͢ͺͨΗΝ͚ͯΖٗনඎ๝౨‫ښ‬୉ָढ़ɽਕؔ͹Γ͑ͶஎదͲ͍Ζͳͺɼʰ‫ͳ͚͞Ͱـ‬͹Ͳ͘ΖʱαϱϒϣʖνɼͯΉΕɼυʖν͹஦͖Δಝ௅ྖΝਫ਼੔͢‫ݳ‬েΝϠυϩԿͤΖ͞ͳ͹Ͳ͘Ζαϱϒϣʖνͳ͏͑қັͲ͍Ζ૱ແ঴ɻฑ੔೧ౕ৚ๅ௪৶പॽযੵੴચਘःAIൠःAIৰਠपम౞टୖ਻ऋ੗ःሑ৷஑যੵੴચ્৲஑যੵੴચয৑भेअऩஷॊ೺ः॑घॊੴ৓॥থআগ‫شॱش‬दँॉ‫ؚ‬ਝੑखञৎभ୳৒॑தइञৗञऩਖ਻पुৌ૪घॊ‫؛‬য৑ऋਝ৒खञ્৒भਖ਻‫઺ق‬൸भ௙શಉ‫ك‬पৌखथ‫ؚ‬਌पਃ༊৾ಆ॑৷ःथੰৠघॊ‫؛‬79઺൸ੳ௙ఠଢੳ௙ঽே੉ୁ૪৶যੵੴચभ੅णਃચযੵੴચधਃ༊৾ಆ఺ल‫ॹؚ‬ॕ‫ش‬উছ‫ॽش‬থॢभঢ়બਙযੵੴચਃ༊৾ಆॹॕ‫ش‬উছ‫ॽش‬থॢ

80যੵੴચभ
੸णभ৶૛ూବ
଍஄৻ਯ

ન૨ଁੑ

DNNऩन

ঋॖ६ଁੑऩन

DNN: Deep Neural Networkযੵੴચभ
੸णभਖ਻ੰৠু১

৚షਖ਻भੰৠীథਖ਻भੰৠ੥ટभ
௓ੑ‫؞‬௓৒

ীథ

81

প୤भ
ਹ৷૭ચ
ऩॹ‫ॱش‬ੑ઴ਃચৡभ਱঱ਃ༊৾ಆ॔ঝ०জ६঒भ৫৅

যੵੴચपਖ਻ੰৠ॑औचॊ঱द੎ਏऩਏಞ

1. যੵੴચधम୦ऊ
2. যੵੴચ॑ણ৷घॊਔଝ
3. যੵੴચभഄఴधॺঞথॻ
4. ॹॕ‫ش‬উছ‫ॽش‬থॢभ৶૛৓ऩూବ
5. ୢ௜पउऐॊযੵੴચभણ৷୩ୠ
6. যੵੴચभ଒ੀधిજऩણ৷
7. যੵੴચ॑ણ৷खञଲષभ৹୸पणःथ

82঩মभযઠ఺लਓ঵ଡୗभ૗৲2010ফ

2060ফ

ৈೡযઠ

ৈೡযઠ

2,948য

3,464য

8,174য

4,418য

েਓফೡযઠেਓফೡযઠযઠ঎‫ॼش‬५਋যઠ঎‫ॼش‬५਋धযઠड़‫ॼش‬५਋੕যઠपಚीॊَেਓফೡযઠ (15ୃ‫ع‬64ୃभযઠ) ُૻ૨ऋಓ౵৓प੗ः૾ଙ‫؝‬਍੼द௹୕ऩௌ௮ৡऋँॉ‫ؚ‬జരযઠऋ૘ऩःञी‫ؚ‬ઇ୘ાृ঺ভ৳௽ાभ଀૿ऋ૘ऩः૾ଙ‫؝‬বੇ੒઴॑৽ੋ৆ੁपஷॉ਱ऐृघऎ‫ؚ‬ऽञ౎বऊैभ਺ৱ॑ళल੢ीॊभद‫ੋ৽ؚ‬ऋણਙ৲घॊ‫؝‬৏઄पऩॊधৈ৾ഄ৲पेॊয੯ા঱ಊधྻฬ৲‫ྻ؞‬ਓ৲पेॊ૘৕৲खथःऎ‫؝‬83যઠ঎‫ॼش‬५਋द৽ੋ৅னपਛ඘खञ৏‫ؚ‬ୢ௜ृফস਑২ऋౄৰखथৈೡ৲ऋਤाযઠड़‫ॼش‬५਋प఍োघॊ‫؝‬ड़‫ॼش‬५धम‫َؚ‬੎෰‫ُ૿଀؞‬धःअਔ௡‫؝‬੍َइैोॊযُऋ੍َइॊযُ॑঱৚ॉ‫ؚ‬঺ভ৳௽ાऩनऋ੎ः଀૿धऩॊञी‫ؚ‬଎ાृฅ໮‫਺ؚ‬ৱऋ೿฽घॊ‫؝‬঩মमযઠड़‫ॼش‬५਋प఍োखथশःऋ‫ે؜‬ীऩৌੁऋऩऔोथऒऩऊढञ‫؝‬যઠड़‫ॼش‬५਋૚বदযઠੴચमবੇਅറपਜ਼઼तऐैोथःॊॻॖॶ঩মॖথॲ५ॺজ‫ش‬4.0
਼ੀਈ੔ഈITবੇ൉ୗ്੉৪বরব

৪বযੵੴચଢ଼஢৫৅ਅറ
ৗ঳৻যੵඈચ৅னੑ઺

਼ੀਈ੔ഈITবੇ൉ୗ്੉ ৯੐घसऌ঺ভ‫؞‬௅‫؝ڭ‬IT ਹણ৷भ஥৲पेॉ౞ਟप਱ऐथਛশघॊ঺ভ
‫؝ڮ‬IT ॑ਹણ৷खञऽठ‫؞‬ऱध‫؞‬खओधभણਙ৲पेॊણৡँॊ঺ভ
‫؝گ‬IT ॑ਹણ৷खञ਍৸‫؞‬਍ੱ‫؞‬௹ऊऔऋৰ૎दऌॊ঺ভ‫؝ڰ‬IT ॑ਹણ৷खञਁુ१‫ش‬অ५ऋডথ५ॺॵউदਭऐैोॊ঺

ਓ঵୓୵৐৏৊஘भ૗୓ऋକऌेअधखथःॊभऊुखोऩः

84

1. যੵੴચधम୦ऊ
2. যੵੴચ॑ણ৷घॊਔଝ
3. যੵੴચभഄఴधॺঞথॻ
4. ॹॕ‫ش‬উছ‫ॽش‬থॢभ৶૛৓ऩూବ
5. ୢ௜पउऐॊযੵੴચभણ৷୩ୠ
6. যੵੴચभ଒ੀधిજऩણ৷
7. যੵੴચ॑ણ৷खञଲષभ৹୸पणःथ

যઠੴચभ3৚भঈ‫ش‬঒ਸ਼1ઃ
ਸ਼2ઃਸ਼3ઃ1950ফ৻ ॥থআগ‫شॱش‬पेॊَ௓૛ُृَർดُ‫ؚ‬ঃ‫ش‬७উॺটথृযੵ‫ع‬ৌਵ३५ॸ঒ELIZA
1980ফ৻ َग़य़५ঃ‫ॺش‬३५ॸ঒ُ‫ؚ‬َই॓४ॕُृَॽগ‫ش‬টُ॥থ‫ع‬আগ‫ॸش‬ॕথॢ2000ফ৻ َॹॕ‫ش‬উছ‫ॽش‬থॢُ‫ق‬2006‫ع‬ਠ૔ऽ ফ‫ك‬ऩनभਃ༊৾ಆ॔ঝ०জ६঒द85ളහऩਖ਻भੰৠऋ൑୔ેীऩੲਾ૪৶ચৡ॑॥থআগ‫شॱش‬ऋ੅ञङ଒৒৓ऩચৡु੅णपधनऽॉ‫ૹؚ‬৷৓पु঳৖َই॓४ॕُ࿌ൗधःअங২َঅॵॢॹ‫ُؚ॑ॱش‬ऒभ৑৅୸खञ॥থআগ‫شॱش‬भੑ઴ચৡ‫ق‬GPU/CPU‫॑ك‬੪प‫ؚ‬ਃ༊৾ಆ॔ঝ०জ६঒॑৷ःॊऒधपेॉ஘‫ر‬ऩୖ਻ੰৠऋदऌॊ஘पऩढञপ୤भ
BIGDATAਹ৷૭ચૠત৲ఏ૆৲ऩॹ‫ॱش‬ੑ઴ਃચGPUৡभ਱঱

ਃ༊৾ಆ
DEEP॔ঝ०জ६LEARNING঒भ৫৅যੵੴચपਖ਻ੰৠ॑औचॊ঱द੎ਏऩਏಞ• ঩মदमऽटजभॖথইছତ૟ऋಥ঱पँॊऋ‫ؚ‬஘‫ر‬ऩ୩ୠदਗ਼৕৲ऋਤ॒दःॊ
• ਲਗदम୻इय৪বभNational Library ofDATAभఏ૆৲‫؞‬ૠત৲ध
BIG DATAभણ৷

Medicine৔भNational Center for
Biotechnology InformationभPubmedऩन‫ؚ‬ఏ૆৲औोञੲਾभૐ஋৲ऋ੔ষखथःॊ
০৏मIoTಉपेॉఏ૆৲औोञ
ੲਾभૐ஋धણ৷ुୖ਻86ৈਙચऩGPUभఃৃपेॉ౰઴ચৡभୖ਻ऋ๞സऔोञ• GPU‫؟‬Graphics Processing Unite
CPU( Central Precessing Unite)पৌखथ‫્ؚ‬प਌प઺൸૪৶पঢ়खथ૪৶ચৡ॑ৈीञुभ‫؛‬CPUऋ‫ؚ‬঳णभ॥॔‫ق‬౰઴૪৶॑ষअ৖ী‫ك‬ऋളහऩੑ઴॑ষअহ॑୛୍पਝੑऔोथःॊभपৌखथ‫ؚ‬GPUदम౐ෞऩੑ઴॑੗ਯధഔ૪৶औचॊহ॑୛୍पਝੑऔोथउॉ‫ؚ‬॥॔ਯु੗ः‫؛‬ॡছक़ॻपेॊGPUਹ৷୭୆઀୹१‫ش‬অ५भ൤఺• ঽ৐दপ୤भGPU॑࿌ൗखञ॥থআগ‫॑شॱش‬ଡണखऩऎथु‫ؚ‬ॡছक़ॻ୭୆दGPUਹ৷୭୆॑઀୹खथऎोॊથમ१‫ش‬অ५ऋ൤఺खथःॊ‫؛‬• ంमMicrosoft Azureभෘ୳ਃ༊पৰ੠प॔ॡ७५खथःॊधऒौ87ͤ઺൸मILSVRC 2013भुभ

DEEP LEARNINGभఃৃ• 2012ফप৫ಈऔोञ઺൸
ੳ௙॥থॸ५ॺ‫ؚ‬Large
Scale Visual Recognition
Challenge 2012(ILSVRC2012)पउःथ‫ؚ‬ढ़ॼॲ‫ॺ؞‬টথॺপ৾भ
Geoffrey Hintonॳ‫ش‬঒ऋॹॕ‫ش‬উছ‫ॽش‬থॢ॑৷ःथ‫ؚ‬෩खऎৈःಖ২द
भ઺൸ੳ௙॑୸ਛ‫؛‬

http://www.imagenet.org/challenges/LSVRC/2013/

The Institute of Electrical and Electronics Engineers
https://spectrum.ieee.org/static/interactive-the-top-programming-languages-201788উটॢছ঒੉ୁ

Pythonभ઎୍IEEE spectrumभউটॢছ঒
੉ୁছথय़থॢपउःथ‫ؚ‬
2016ফप3ਜ਼दँढञ
Pythonऋ2017ফपຄप1ਜ਼प঱ಊ‫؛‬যઠੴચ‫؞‬ਃ༊৾ಆभਹ৷୭୆ऋইজ‫ش‬दତइैो‫ؚ‬঺ভৰಎभയਤप๾൴যੵੴચਹણ৷भञीभ૮મউছॵॺইज़‫ش‬঒ਃ༊৾ಆृॹ‫ॱش‬૪৶ऋල౐पষइॊছॖঈছজͤ॑੗ਯఏ૆ಎ૟खञPythonभDistribution‫ق‬ਹ৷୭୆॑ঃॵॣ‫ش‬४৲खञुभ‫ुك‬ఃৃͤँॊ્৒भਃચ॑੅णউটॢছ঒॑৒஑৲खथ‫౎ؚ‬भউটॢছ঒ऋਬ৷दऌॊ૾ଙपखञुभ॑‫ؚ‬ളਯૐीथऽधीञই॓ॖঝ

89ঈছक़२঱दJupiter notebookधःअ
Pythonभॖথॱ‫ش‬ইख़‫ش‬५ुણ৷૭ચ

৻਀৓ऩDNNͤছॖঈছজ

ͤDNN‫؟‬Deep Neural Network

DNN॑৷ःथDeep Learning औचॊ

TensorFlow
googleऋ৫৅खञDNNছॖঈছজ‫؛‬googleऋৰ੠पઞढथःॊছॖঈছজ॑ड़‫ش‬উথ৲खथःॊ‫؛‬https://www.tensorflow.org/versions/master/tutorials/

Chainerব৔ঋথॳক‫ش‬੫঵दँॊPreferred Networksऋ৫৅खञDNNছॖঈছজ‫ ؛‬Preferred NetworksमMicrosoftधभੈ঵ृব৔૚পু੫঵ध઀௚खथःॊ‫؛‬ऒठैुছॖঈছজ॑ड़‫ش‬উথ৲खथःॊ‫؛‬https://docs.chainer.org/en/stable/guides/90

1. যੵੴચधम୦ऊ
2. যੵੴચ॑ણ৷घॊਔଝ
3. যੵੴચभഄఴधॺঞথॻ
4. ॹॕ‫ش‬উছ‫ॽش‬থॢभ৶૛৓ऩూବ
5. ୢ௜पउऐॊযੵੴચभણ৷୩ୠ
6. যੵੴચभ଒ੀधిજऩણ৷
7. যੵੴચ॑ણ৷खञଲષभ৹୸पणःथযੵੴચभ੸णभ৶૛ూବ଍஄৻ਯ

ન૨ଁੑ

DNNऩन

ঋॖ६ଁੑऩन91যੵੴચभ3णभ৾ಆ஘ૄઇపँॉ৾ಆઇపऩख৾ಆਘ৲৾ಆযઠੴચ॑ણ৷खञਖ਻ੰৠभ੦ম৓ऩೳੌा1‫ੰ ك‬ৠखञःਖ਻पৌखथੑ઴ঔॹঝ॑ৠीॊ2‫ ك‬௬੼ঢ়ਯ‫ق‬௤ଷঢ়ਯ‫॑ك‬ৠीॊ3‫ ك‬௬੼ঢ়ਯ‫ق‬௤ଷঢ়ਯ‫॑ك‬ਈপ৲ुखऎमਈ৵৲घॊ

92যઠੴચ॑ણ৷खञਖ਻ੰৠभ੦ম৓ऩೳੌा
੎৚షীෲ॑୻पधॊध1‫ك‬ੰৠखञःਖ਻पৌखथੑ઴ঔॹঝ॑ৠीॊэ৚షૄ‫ق‬৯৓૗ਯ॑ହ৥૗ਯदੑ઴घॊૄ‫॑ك‬ਝ৒
y= a1x1+a2x2+‫؞؞؞؞‬akxk+b2‫ك‬௬੼ঢ়ਯ‫ق‬௤ଷঢ়ਯ‫॑ك‬ৠीॊэਈ৵੸ଭ১

3‫ك‬௬੼ঢ়ਯ‫ق‬௤ଷঢ়ਯ‫॑ك‬ਈপ৲ुखऎमਈ৵৲घॊэَజര૗ਯभ೾৒கध‫ؚ‬ஆয়૗ਯभ೾৒கउेल৚షૄ॑৷ःथ੷ीञ௓৒கभ୷भ੸ଭਮෙ୷ُऋਈ৵पऩॊेअप৚షૄभબਯ॑੷ीॊোৡಽর৑ಽ‫ق‬ඛोಽ‫ك‬লৡಽ३থউঝऩ
DNNঔॹঝ୻

DNNमোৡಽ‫ؚ‬র৑ಽ‫ق‬ඛोಽ‫ؚك‬লৡಽऊैऩॊ‫؛‬র৑ಽ॑୦ಽ઼ऎऊ‫ؚ‬ඛोశ଍஄૗ఌ uମzಽभঀ‫॑ॻش‬ःऎण઼ऎऊपणःथु঄ॺऋਝ৒घॊ૑ਏऋँॊ‫؛‬ঃছও‫شॱش‬
Wभகममगीছথॲ঒प
ਝ৒घॊ‫؛‬঱੶म৚షঔॹঝ‫؛‬ীథਖ਻भৃ়मলৡಽyऋളਯधऩॊ‫؛‬93శ଍஄૗ఌप৷ःॊણਙ৲ঢ়ਯ
Reluঢ়ਯ

३ॢঔॖॻঢ়ਯ۩1

۩

Z=F(u)=1/(1+e-u)

Z=F(u)=max(0,u)

uuuभ଀भகम૮ଳ‫ؚ‬ਯகभপऌऔुઅൟदऌॊ௬੼ঢ়ਯ‫ق‬௤ଷঢ়ਯ‫॑ك‬ৠीॊ৚షਖ਻भधऌ਴಑੸ଭෙ୷Mean Squared Error॑৷ःॊáীథਖ਻भधऌ९ইॺঐॵॡ५ঢ়ਯ॑৷ःथॡট५ग़থॺটআ‫ش‬ෙ୷॑੷ीॊ‫ق‬૞અ‫ك‬\-t)2९ইॺঐॵॡ५ঢ়ਯ
f(x) = (exp(xi)/j exp(xj))d i=1ॡট५ग़থॺটআ‫ش‬ෙ୷á(hL)=ƷL \LORJK/L î\L ORJ îK/L ৰ੠मChainerছॖঈছজಉभরप৒ଝऔोथःथ
F.softmax_cross_entropy(y,t)ಉधোৡघोयੑ઴खथऎोॊ

94௤ଷঢ়ਯ॑ਈ৵৲घॊ
‫ٔق‬जभञीभঃছও‫ॱش‬w॑੷ीॊ‫ك‬áਈ૶ఋৣ১1)ছথॲ঒प‫ۦ‬भੂ਋க॑ৠी
ॊ
2‫ك‬௤ଷঢ়ਯभ๶ীக‫ق‬ൊऌ‫ك‬॑੷ीॊd/dw á3)ঃছও‫॑ॱش‬ಌৗघॊ௤ଷঢ়ਯभ๶ীக॑ਬऎ
wବw - d/dw á (¼ǐ‫৾؟‬ಆબਯ‫ك‬‫ۦ‬1‫ۦ‬0‫ق‬૞અ‫ك‬৯৓શपਈి৲औोञNeural Network઺൸ੳ௙ эConvolutional Neural Networkৎ௺ഔੰෲ э Recurrent Neural Network95

1. যੵੴચधम୦ऊ
2. যੵੴચ॑ણ৷घॊਔଝ
3. যੵੴચभഄఴधॺঞথॻ
4. ॹॕ‫ش‬উছ‫ॽش‬থॢभ৶૛৓ऩూବ5. ୢ௜पउऐॊযੵੴચभણ৷୩ୠ
6. যੵੴચभ଒ੀधిજऩણ৷
7. যੵੴચ॑ણ৷खञଲષभ৹୸पणःथୢ௜पउःथु஘‫ر‬ऩ୩ୠदযઠੴચभણ৷ऋ਋ୄऔोथःॊ঳णभীథ্১धखथ‫ؚ‬੕ਜ੄َICTभਤ৲ऋ౻৷ध௮ऌ্प఺ऻघ୶஭पঢ়घॊ৹ਪଢ଼஢ُपउःथ௙શ‫ؚ‬੒೾‫ؚ‬ৰ஍पীథखथःॊ‫؛‬ऒोै॑ୢ௜पසैख়ॎचॊऒधपेॉ‫ؚ‬AI॑ણ৷खथनभेअऩহऋৰਠ૭ચऊઅइॊুऋऊॉपऩॊऊुखोऩः‫؛‬96ୢ௜पउऐॊযੵੴચभણ৷भ఻৬୻
ใपୢ௜पउऐॊযੵੴચभਹણ৷भহ୻म்ઁऎோ૔खथउॉ‫ؚ‬ੜइਢऐथःॊञी‫ؚ‬घसथ॑ງஂघॊभम൑୔धऩढथःॊ‫؛‬ऒऒदम঳৖भ୩ୠपणःथআॵॡ॔ॵউघॊ
1‫ك‬ୢ௜઺൸૪৶
2‫ك‬ऋ॒भPrecision Medicine
3‫ك‬ঽே੉ୁ૪৶ಉभણ৷઺൸૪৶‫઺؞‬൸ੳ௙मਸ਼3ઃযੵੴચঈ‫ش‬঒भౌહऐ૽धऩढञ୩ୠभ঳णदँॉ‫ؚ‬ણ৷ऋ્प੔ষखथःॊী৙दँॊ‫؛‬઺൸൧૵‫ؚ‬୰৶൧૵ृ೩঻भਗ௴ऊैभႈ୰੒೾ಉ਄ॉੌऽोथःॊ‫؛‬1‫ك‬ୢ௜઺൸૪৶

TED Stanfordপ৾Fei Fei Li੽भউঞ८থॸ‫ش‬३ঙথ

https://www.ted.com/talks/fei_fei_li_how_we_re_te
aching_computers_to_understand_pictures?langu
age=ja97

যੵੴચपेॊ౼৿ဿᯉಉभ൧૵੍ର

যੵੴચ॑ણ৷खञ୒রဌச୰৶൧૵੍ର

98

2‫ك‬ऋ॒भPrecision Medicineবয়ऋ॒ଢ଼஢७থॱ‫ش‬যੵੴચ॑৷ःञଁ়৓ऩऋ॒ ୢ௜३५ॸ঒भ৫৅

https://www.ncc.go.jp/jp/information/pr_release/2016/1129/press_release_20161129_01.pdf

3‫ك‬ঽே੉ୁ૪৶ಉभણ৷⥲ົ┬ ḟୡ௦ேᕤ▱⬟♫఍ᐇ⿦㼃㻳 ⮬↛ゝㄒฎ⌮䛾་⒪ᛂ⏝http://www.soumu.go.jp/main_content/000474414.pdf

99

100

1. যੵੴચधम୦ऊ
2. যੵੴચ॑ણ৷घॊਔଝ
3. যੵੴચभഄఴधॺঞথॻ
4. ॹॕ‫ش‬উছ‫ॽش‬থॢभ৶૛৓ऩూବ
5. ୢ௜पउऐॊযੵੴચभણ৷୩ୠ
6. যੵੴચभ଒ੀधిજऩણ৷
7. যੵੴચ॑ણ৷खञଲષभ৹୸पणःथ

যੵੴચभ଒ੀາ৾৓‫؞‬໪୛৓ऩਖ਻ऊै‫୒ૼؚ‬৓‫؞‬ৰᄷ຃৶৓‫؞‬১৓ୖ਻ऽद1‫ॳك‬গ‫ش‬জথॢॸ५ॺ2‫ਂك‬౥৸ਙ৒৶
3‫ك‬রবୁभ৖ો
4‫ك‬ইঞ‫ش‬঒ਖ਻
5‫੶ك‬ಀம৉ਖ਻6‫ॺك‬টॵ॥ਖ਻
7‫ك‬᭩ᮮऋँढञৃ়भிભभਚ૔

101

1‫ॳك‬গ‫ش‬জথॢॸ५ॺ

2‫ਂك‬౥৸ਙ৒৶

3‫ك‬রবୁभ৖ો

4‫ك‬ইঞ‫ش‬঒ਖ਻ॳগ‫ش‬জথॢॸ५ॺधःअ‫ؚ‬ਃ༊पૢ௦औचथয৑ऋૢ௦खथःॊऊਃ༊ऋૢ௦खथःॊऊ‫ؚ‬ਖ਼૵घॊॸ५ॺऋँॊ‫؛‬30٫भଟਪ৩पযटधਖ਼૵औचोय়તद‫ؚ‬2014ফप33٫
पथ୸ਛमऔोञऋ‫ؚ‬౞टેীपয৑ैखःખૢदँॊधःअਛટ॑଱ऑथमःऩः‫؛‬َঽഞ੉఺भঃছॻॵॡ५ُःॎॅॊَॡঞॱযमመणऌटधॡঞॱযम੉ढञُ॑‫ؚ‬।‫ش‬।ঝऋਯ৾৓प઒৥खञुभ‫؛‬౥৸ऩযੵੴચमजभ౥৸ਙ॑઒৥दऌऩःधःअဤ࿬ऋেगॊ‫؛‬ਗ৖ऊै૖੯‫؞‬৒ଝ॑ਝ৒घॊ૑ਏऋেगॊ‫؛‬রবୁ॑ੴढथःॊ঄ॺऋ॥থਠ૔‫ؚ‬যੵੴચमઓઅघसऌ෇೧আগ‫شॱش‬पৌखथরবୁ॑োৡ‫ृ ؚ‬ถ಍২भৠ৒॑যੵੴચঽମदશभ৖ોदরবୁ॑ੴैऩः঄ॺ दऌऩः‫ँ؛‬ॊ৯৓॑୸ਛઞ৷धऋরবୁ॑੶ಀधखथੳ௙खथ‫ ؚ‬घॊৃ়‫ؚ‬ঢ়બँॊহඨभऺऊ‫ؚ‬ँॊ੶ಀपৌखथमऒभ੶ಀ‫ؚ‬धःअेअपନ௦घॊध‫ৄؚ‬ऊऐ঱ ঢ়બ૮ःহඨम૮଒पோ૔घॊञরবୁ॑৶ੰखथःॊ঄ॺऋৌૢ ी‫ؚ‬जोຸ॑ःলजअधघॊध૮खथःॊ஘पৄइॊ‫౐؛‬प੶ಀ॑ ଒पৎ৑॑ਏख೿ૃखथखऽअ‫؛‬ඝ੿घॊटऐदੴચ॑३঑গঞ‫ ش‬ਠ૔भधऒौ‫ؚ‬যੵੴચपৌख঄ॺखथउॉ‫ؚ‬ੴચम঄ॺपुਃ༊ ॺऋَిજُऩ෇೧॑৒ीॊ૑ਏपुदढठ঱ऑैोॊधःअुभ‫؛‬ऋँॉ‫෇َؚ‬೧॑৒ीॊਖ਻ُभ঳ಹ৓ऩੰৠम൑୔दँॊ‫؛‬102

5‫੶ك‬ಀம৉ਖ਻

6‫ॺك‬টॵ॥ਖ਻ॺটॵ॥ऋ଍ଡ଼भ঱॑௒ষखथउॉ‫ؚ‬วଡ଼ऋਝऐैोथःॊ‫؛‬଍ଡ଼भ੔पमఊ৶੿঵রभ5੡भ੿঵৩ऋउॉ‫ؚ‬जभऽऽ௒ॊध5੡ऋ‣ऊोॊ‫؛‬શभวଡ଼भ੔पम1যऋ੿঵खथःॊ‫؛‬A੽मञऽञऽ
ॺটॵ॥भਤষ্਱॑૗इॊঞং‫ش‬भ੺ऎपःॊ‫؛‬A੽ऋधॊस
ऌَਫखःُষ৿धमनभेअऩ
ुभऊ‫؛‬धःअਖ਻‫؛‬যੵੴચपَਫखःُਖ਼૵॑औचॊৃ়‫ؚ‬୦॑੅ढथَਫखःُधघॊऊ‫؛‬যੵੴચपनभेअऩ੼கਖ਼૵॑ଖ
इॊसऌऊ‫ؚ‬नभेअप॥থ७থ
१५॑੭ॊभऊ‫ؚ‬॥থ७থ१५म
੭ैोॊभऊ‫؛‬੶ಀ॑ৰ਼ੀभਔ௡ध੥लण
ऐैोॊऊधःअਖ਻‫؛‬॥থআগ‫شॱش‬म੶ಀभਔ௡॑৶
ੰखथःऩःभद‫੶ؚ‬ಀभඝ੿टऐदੴચ॑ৰਠदऌऩः‫؛‬୻इय‫ؚ‬३ঐٔक़ঐٙ३ঐक़
ঐधःअ஘ऩ໪୛॑঳ಹ৲ख
थඃ੭घॊহमदऌऩः‫؛‬7‫ك‬᭩ᮮऋँढञৃ়भிભभਚ૔যੵੴચ॑࿌ൗखञଲષ॑ઞ৷खथକऌञਖ਻भরद‫ؚ‬যੵੴચभਖ਼૵ऋෙढथःञञीपକऌञਖ਻भிભम‫ؚ‬ଲષଲୗ঻पँॊऊ‫ؚ‬ଲષઞ৷঻पँॊऊ‫؛‬যੵੴચऋলखञੰ௦पజढथକऌञਖ਻भਚ૔म‫ؚ‬नभेअऩৃ়पनऒप઼ऎसऌऊ‫؛‬103

઻ড় ITS ଡ୳‫؞‬ট‫ॻش‬ঐॵউ 2017ITS‫ق‬Intelligent Transport Systems‫؟‬ৈ২੊ଡ଼ઐৢ३५ॸ঒‫ك‬ঽ৿ઈૡभী৙दमAIણ৷पेॉেगञிભभਚ૔ु৮૛औोथःॊٛৈ২ঽ৿ઈૡभ਑২ତ૟पબॊ੦ম৓અइ্ٜৈ২ঽ৿ઈૡभৰਠभञीभ਑২ତ૟भ্ଉ‫ق‬পഹ‫ك‬भਫ਼ୈपँञढथम‫ؚ‬೑ऋবधखथम‫ؚ‬ਰৣभ੦ম৓ऩઅइ্‫ق‬ਅറ‫ك‬प੦तःथਫ਼ୈ॑ষअु भधघॊ‫؛‬ϸ‫؝‬র਋৓ଳਡपয়ढञ਑২એपउऐॊব੠৓জ‫شॲش‬३ॵউभ৅มϹ‫؝‬਍৸ਙ॑ન৳खणणॖঀঋ‫ش‬३ঙথऋയਤऔोॊेअऩ਑২ೳੌाभੁ৒Ϻ‫঺؝‬ভਭઍਙ॑৐઀धखणणॖঀঋ‫ش‬३ঙথऋയਤऔोॊेअऩிભঢ়બभ ৥ન৲যੵੴચभిજऩણ৷
Asilomar AI Principles (2017)2017ফ1া‫ؚ‬ढ़জইज़ঝॽ॔ପ॔३টঐपथষॎोञ‫ؚ‬৸਼ੀभAIभଢ଼஢঻ध৽ੋ৾‫ؚ‬১൅‫ؚ‬຃৶‫ؚ‬າ৾भ௧୅ੇऋૐऽॉ৮૛औोञযథपधढथથஇऩAIपঢ়घॊਉಋ‫؛‬ଢ଼஢पঢ়घॊ5ඨ৯‫ؚ‬຃৶ध੼க௴पঢ়घॊ13ඨ৯‫ؚ‬শ਋৓ऩਖ਻पঢ়घॊ5ඨ৯ੑ23ඨ৯ऋ଱ऑैोञ‫؛‬AIॿॵॺড‫ش‬ॡ঺ভ௓ਤভ৮ਾઔછ2017੕ਜ੄ੲਾৢਦ৆ੁଢ଼஢ਚऋ‫ؚ‬਴ਛ28ফ10াऊैَAIॿॵॺড‫ش‬ॡ঺ভ௓ਤভ৮ُ॑৫ಈख‫ؚ‬AIॿॵॺড‫ش‬ॡ৲॑ीएॊ঺ভ
৓‫ੋ৽؞‬৓‫؞‬຃৶৓‫؞‬১৓ऩୖ਻पणःथਫ਼ୈ॑ਤीथःॊ‫؛‬਴ਛ29ফ7াपਾઔછऋ৅লऔो‫ؚ‬जभরद‫ؚ‬ব੠৓ऩ৮૛भञी
भAI৫৅फ़ॖॻছॖথ੧ऋ઀੧औोञ

104

Asilomar AI Principles (2017)• Research Issues• 1) Research Goal: The goal of AI research should be to create not undirected intelligence, but
beneficial intelligence.
• 2) Research Funding: Investments in AI should be accompanied by funding for research on
ensuring its beneficial use, including thorny questions in computer science, economics, law, ethics,
and social studies, such as:
How can we make future AI systems highly robust, so that they do what we want
without malfunctioning or getting hacked?
How can we grow our prosperity through automation while maintaining people’s
resources and purpose?
How can we update our legal systems to be more fair and efficient, to keep pace with AI,
and to manage the risks associated with AI?
What set of values should AI be aligned with, and what legal and ethical status should ithave?
• 3) Science-Policy Link: There should be constructive and healthy exchange between AI researchersand policy-makers.
• 4) Research Culture: A culture of cooperation, trust, and transparency should be fostered among
researchers and developers of AI.
• 5) Race Avoidance: Teams developing AI systems should actively cooperate to avoid cornercutting on safety standards.

• Ethics and Values• 6) Safety: AI systems should be safe and secure throughout theiroperational lifetime, and verifiably so where applicable and feasible.
• 7) Failure Transparency: If an AI system causes harm, it should be possible
to ascertain why.
• 8) Judicial Transparency: Any involvement by an autonomous system injudicial decision-making should provide a satisfactory explanation
auditable by a competent human authority.
• 9) Responsibility: Designers and builders of advanced AI systems are
stakeholders in the moral implications of their use, misuse, and actions,with a responsibility and opportunity to shape those implications.
• 10) Value Alignment: Highly autonomous AI systems should be designed
so that their goals and behaviors can be assured to align with humanvalues throughout their operation.
• 11) Human Values: AI systems should be designed and operated so as tobe compatible with ideals of human dignity, rights, freedoms, and culturaldiversity.
• 12) Personal Privacy: People should have the right to access, manage andcontrol the data they generate, given AI systems’ power to analyze andutilize that data.

105• 13) Liberty and Privacy: The application of AI to personal data must
not unreasonably curtail people’s real or perceived liberty.
• 14) Shared Benefit: AI technologies should benefit and empower as
many people as possible.
• 15) Shared Prosperity: The economic prosperity created by AI
should be shared broadly, to benefit all of humanity.
• 16) Human Control: Humans should choose how and whether todelegate decisions to AI systems, to accomplish human-chosen
objectives.
• 17) Non-subversion: The power conferred by control of highly
advanced AI systems should respect and improve, rather than subvert,
the social and civic processes on which the health of society depends.
• 18) AI Arms Race: An arms race in lethal autonomous weapons
should be avoided.

• Longer-term Issues• 19) Capability Caution: There being no consensus, we should avoid
strong assumptions regarding upper limits on future AI capabilities.
• 20) Importance: Advanced AI could represent a profound change inthe history of life on Earth, and should be planned for and managedwith commensurate care and resources.
• 21) Risks: Risks posed by AI systems, especially catastrophic orexistential risks, must be subject to planning and mitigation efforts
commensurate with their expected impact.
• 22) Recursive Self-Improvement: AI systems designed to recursively
self-improve or self-replicate in a manner that could lead to rapidly
increasing quality or quantity must be subject to strict safety andcontrol measures.
• 23) Common Good: Superintelligence should only be developed in
the service of widely shared ethical ideals, and for the benefit of all
humanity rather than one state or organization.

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1. যੵੴચधम୦ऊ
2. যੵੴચ॑ણ৷घॊਔଝ
3. যੵੴચभഄఴधॺঞথॻ
4. ॹॕ‫ش‬উছ‫ॽش‬থॢभ৶૛৓ऩూବ
5. ୢ௜पउऐॊযੵੴચभણ৷୩ୠ
6. যੵੴચभ଒ੀधిજऩણ৷
7. যੵੴચ॑ણ৷खञଲષभ৹୸पणःथ‫ؽ‬փָसΝ‫؜‬Ξͫਕ޽எ೵੣඾͹֋൅ӣ༽ϓϫʖྭϐΠϨϱήʹ͹Γ͑ͶԢ༽ͪ͢͏͖ɼʹ͹Γ͑͵՟ୌΝմ݀ͪ͢͏͖ɽϐΠϨϱήͨΗ͖Ն೵͖ʃͤ͵Κͬɼυʖν੖ཀྵυʖν͹੖ཀྵͳϧϗϩ෉͜ඉߑଆԿυʖν͹ߑଆԿϠυϩ͹ߑ஛ɿ‫ࢥگ‬υʖνͺ͍Ζ͖ɿ෾ੵՆ೵͵ϓΧʖϜρφʤ&69͵ʹʥ͖ɿͨ΍ͨ΍ਕ޽எ೵͗චགྷ͖υʖνΝਕ޽எ೵Ͳ෾ੵͲ͘Ζ‫ܙ‬ͶߑଆԿմ݀ͪ͢͏՟ୌͶ߻Κͦͱ෾ੵϠυϩΝߑ஛ͤΖ‫ৄݗ‬໪దΝୣ੔Ͳ͘Ζड़ྙ݃Վ͓͗ΔΗͱ͏Ζ͖‫ͤৄݗ‬ΖՀӣ༽Հӣ༽Ͳ‫ࣰݳ‬υʖνͲ໲ୌໃ͏͖ɼΉͪ͠Δ͵Ζυʖν͹फॄߑଆߑ஛࠸‫ৄݗ‬ӣ༽৏໲ୌໃ͏͞ͳΝ֮೟εητϞ౹߻‫ع‬ଚεητϞΏΨϘϪʖεϥϱͳ͹౹߻ΝਦΖ108

ిજऩ३५ॸ঒৫৅भञीपम‫ؚ‬ਠৃभਖ਻ਔ௙ध‫ؚ‬যੵੴચ৫৅ଢ଼஢঻ध‫ؚ‬ঘ‫ش‬२‫ش‬ॖথॱ‫ش‬ইख़‫ش‬५৫৅ঋথॲ‫ش‬धभ৑द‫ؚ‬ഡऩ॥঑গॽॣ‫ش‬३ঙথऋ૑ਏधऩॊ‫؛‬जो॑৶ੰखञ঱द‫ؚ‬ৌૢిભ঻॑௧ભघॊ૑ਏऋলथऎॊ‫؛‬ऽञ‫ؚ‬ಖ২਱঱भञीपम‫ؚ‬ਹ৷୭୆दেऽोॊॹ‫৾॑ॱش‬ಆऔचਢऐॊலੌा੿ॉु੎ਏदँॊ‫؛‬঩মमॖঀঋ‫ش‬३ঙথચৡऋ଩ःध௬੼औोथःॊ

਴ਛ29ফගৢૹஜછ ৽ੋਓ঵੄
http://www.meti.go.jp/report/tsuhaku2017/pdf/2017_00-all.pdf

109ॖঀঋ‫ش‬३ঙথप਱ऐथষ৿खथষऎऒधऋ੎ਏ

110