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AlphaGo에서 시작하는 인공지능
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Leonardo YongUk Kim
December 11, 2021
Technology
1
320
AlphaGo에서 시작하는 인공지능
AlphaGo, Alpha Zero에서 시작해서 여러 인공지능의 개념을 살펴봅니다.
Leonardo YongUk Kim
December 11, 2021
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Transcript
LEONARDO YONGUK KIM
[email protected]
ALPHAGOীࢲ दೞח ੋҕמ
ALPHAGOোഄ AlphaG o Fan Goח ੌࠄয۽ ߄قਸ 2015 2016.
3. 9 AlphaG o Lee 2017. 5. 17 AlphaG o M aster AlphaG o Zero 2017. 10. 19 2018.12.7 Alpha Zero ౸ റ (2ױ)җ Ѿ೧ࢲ थܻ ࣁج (9ױ)җ Ѿ೧ࢲ थܻ ӂ ೧Ѿػ 16݅ ӝࠁ ण. 48ѐ TPU ৡۄੋ Ҵ 60োथ ழઁীѱ थܻ 4ѐ TPU. 10ߓ ীց ബਯ ੋр धহ (ӝࠁ X) ߄ق ࠂ ঌҊ ݃झఠ৬ ऱਕ 89थ 11ಁ ࣳӝ, झ, झఋ 2 ࠂ Goܳ ܴীࢲ ઁ৻
ݾ ࣻ ੍ӝ न҃ݎ ъച ण ੋҕמ ࢎਊ
ࣻ ੍ӝ MiniMax Monte Carlo Method Monte Carlo Tree Search
MINIMAX
TIC TAC TOE O O O X X O X
O X X O X O - ݢ 3ѐܳ աۆ ֬ ࢎۈ ӝח ѱ - ب ࠂೠ ѱ ݽٚ ҃ ࣻܳ ࠅ ࣻ . - 9 x 8 x 7 x 6 x 5 x 4 x 3 x 2 = 9! O X O O X X O
O O O O O O O O O ROOT
O X O X O X O X O X O X O X O X O X O O X O O X O O X O O X O O X O O X O ֢٘ 1 0 -1 0 -1 -1 ۚ ܖ ӝݶ 1, ࠺ӝݶ 0,ݶ -1 ROOT ੑীࢲ धۄҊب פ. ݽٚ ֢٘ח ध ࠗݽੑפ.
O O O O O O O O O ROOT
O X O X O X O X O X O X O X O X O X O O X O O X O O X O O X O O X O O X O ֢٘ 1 0 -1 0 -1 -1 ۚ ܖ MY TURN ࣻܳ ࢶఖೞҊ ࣻо ࠳ےܳ ઁѢ
O O O O O O O O O ROOT
O X O X O X O X O X O X O X O X O X O O X O O X O O X O O X O O X O O X O ֢٘ ۚ ܖ YOUR TURN ࣻܳ ࢶఖ ࣻо ח ࠳ےח ઁѢ
O O O O O O O O O ROOT
O X O X O X O X O X O X O X O X O X O O X O O X O O X O O X O O X O O X O ۚ MY TURN ࣻܳ ࢶఖ
None
- (19 x 19)! - 19 x 19 = 361
- 361 x 360 x 359 x 358 …. x 2 - 26744876149564427899473201526425013452390919904351815721084971068304474 7437531294143149639831010372677443849403182318969228741381559487197927737 64930851408087543453474101182344879484162985721534603948370802204778391 45379274006646833128661312942336287321284636912937632439789397222224742 52826712518506072707918591157844247991603554375217925635775598044364577 67819229829195896785070533331329604395837235880245012197523337773352603 746540435758711323413067205097510873318696774954051195138779582025728045 717997197429383169516478847881483048003766654327470766455887103023601081 7570107837589904730596477443151082000948524919032642496288011069869044 42993333787797164945029657423253487692054233010201128993815319944149127 636942433924747935483481769568213401600000000000000000000000000000000 0000000000000000000000000000000000000000000000000000000000000000 ߄ق ҃ ࣻ
MONTE CARLO METHOD
ਗਯਸ য ࠇद - ਗਯਸ णפ. - ਗਯਸ ా҅ਵ۽ ҳೡ
ࣻ ਸөਃ?
MONTE CARLO METHOD - ےؒೞѱ ਸ ନद. - ਗ উ
іࣻо 314ѐ, ࢎпഋ উ іࣻо 400ѐۄ о೧ ࠇद. - 4(R ^ 2) : π(R ^ 2) = 400 : 314, π = 3.14 - ࠙ ਸ ݆ ନਸ ࣻ۾ ؊ ೠ ਗਯਸ ঌѱ ؾפ.
ޅ ࠊب ݆ ࠁݶ ঋਸө?
O O O O O O O O O ROOT
ࣻ റ ےؒਵ۽ 10౸ فয пп थܫਸ ҳೣ. 40% PLAYOUT 60% 30% 20% 80% 30% 20% 50% 10% - п ࣻ ݃ റ, ےؒೞѱ 10౸ਸ فয (Playout) थܫਸ ҳೣ. (9 x 10 = 90౸) - थܫ о ֫ 5ߣ૩ ࣻ(80%)о ୭ҊҊ ౸ױೣ. - दр ؊ ݆ ݶ Playout പࣻܳ ט۰ࢲ न܉بܳ ૐоदఆ ࣻ . (100౸ فӝ) - Playout പࣻо טযաݶ טযզ ࣻ۾ MiniMax Ѿҗ৬ ਬࢎ೧ Ѫ. - Playout, Rollout, Simulation ١ ױযо ࢎਊ ؽ.
Ӓېب ࣻ ৻ী ݽف ےؒ ખ Ӓۧ ঋաਃ?
MONTE CARLO TREE SEARCH
O O O O O O O O O ROOT
40% PLAYOUT 60% 30% 20% 80% 30% 20% 50% 10% - ےؒೞѱ فযࢲ थܫ ֫ ࣻܳ ݢ Ҋܵפ.
O O O O O O O O O ROOT
- ઁ थܫ ֫ 5ߣ૩ ࣻ ਤ۽ ےؒ Ѿਸ פ. (70%) - աݠ ٜࣻب ےؒ Ѿਸ оՔפ. (30%) - ֬ ࣻо ӝ ٸޙੑפ.
O O O O O O O O O ROOT
- 5ߣ૩ ࣻী ೧ যו ب ےؒ Ѿਸ ೮ਵݶ, 5ߣ૩ ࣻ धب ܻী ನೣפ. - ઁ ےؒೞѱ ف݅ ف ࣻח ҊೞҊ פ. - ର ৌ۰ ח ֢٘о ݆ই פ. X O X O X O O X X O O X O X O X
None
- झח 1996֙ 2ਘ 10ੌ MiniMax ۽ ࠂ. (गಌ ஹೊఠ
٩ ࠶ܖ) - ୭Ӕীח Monte Carlo Tree Search۽ ോಪਵ۽ب Ӓے٘݃झ ఠܳ ӣ. - ೞ݅ ߄ق ࠛоמ೮. - ഛܫਵ۽ ೞӝূ ڜࣻо ցޖ ݆ . ࣻ ੍ӝ݅ਵ۽ ࠙ೠоਃ?
ח Ҕ݅ ٮઉࠁݶ উغਃ?
(ࢎۈ ࢤпী) ח Ҕ݅ ٮઉࠁݶ উغਃ?
न҃ݎ न҃ݎ CNN
न҃ݎ
ӝ नഐܳ ߉ই оҕ೧ ӝ नഐܳ ࠁղח Ѫ? ۠
X1 X2 Y W1 W2 Y = W1X1 + W2X2
ࣻ ݆ ۠ Ѿ೧ (֎ਕ) מਸ ٜ݅যմ Ѫ ۢ, ࣻ рױೠ о ো(ੋҕ ۠)ਸ ֎ਕܳ ٜ݅ݶ যڌѱ ؼөਃ?
- ӝ 0~9ܳ Ҵ NISTо ࣻ. - о۽ 28, ࣁ۽
28 ࣄ۽ ҳࢿ. - ೠ Ӗ 784 ࣄ (28 x 28) MNIST
None
- ࡈр࢝ ਵݶ উػח ڷ. - ۆ࢝ ਵݶ
જח ڷ. - 0 оؘח ਵݶ উػ. - 1 оؘח ਸ оמࢿ ֫ . - ࡈъ -1, ی 1۽ ࠁ. Ѩ ࢝ 0 ӝ ࠂೞ.
X1 X784 W1 W784 W1X1 + … + W784X784 =
Y Yо ݶ ӝо 5ۄח ڷ.
- W1X1 + … + W784X784 = Y - X1ࠗఠ
X784ө ֎ਃ? ੑ۱ 784ѐ - W1ࠗఠ W784ө ֎ਃ? оо 784ѐ - ইۄ࠺ই ंо 0ࠗఠ 9өਗ਼ইਃ? оо 7840ѐ (784 x 10) न҃ݎ
X1 X2 Y1 W1 W2 X784 … Y2 Y10 …
784ѐ ੑ۱ 0ੋоਃ? 1ੋоਃ? 9ੋоਃ? ߣ૩ ࣄ W10 W7840 7840ѐ о 10ѐ ۱
X1 X2 Y1 X784 … Y2 Y10 … ੑ۱க ۱க
оח ־о աਃ?
- ӝ҅о ҕࠗೞӝ ٸޙী ӝ҅ ण - ӝ҅о ҕࠗೡ ࣻ
ѱ ޙઁ৬ ਸ ળ࠺פ. - MNISTۄח ӝ ࣁب ৬ э ઓפ. - ف ઙܨ ؘఠܳ ৮ ܻ࠙ೡ ӝӝ৬ ಞਸ ӝ҅о ইմҊ ࠁݶ ؾפ. - ण ؘఠܳ оҊ ࣻ হ ࠙ਸ ߈ࠂ ೞݴ оܳ ઑӘঀ Ҋоݴ ৢ߄ܲ ݽ؛ਸ ٜ݅যцפ. - ਸ Ҋ णਸ दఃח Ѫਸ ب णۄҊ פ. ӝ҅ण
None
- Ӓېਸ ਤ೧ GPUח ࣻ হ Ӓܿ৬ ࡄ ై ؼ
ܳ ҅ೞҊ ژ ҅פ. - GPU ఌਗೡ ҅ מ۱ ݠन۞җ ঐഐ ചತ ী פ. - ߈ݶ GPUח ਬোೠ ౸ױਸ ޅפ. - 1950֙ ࠗఠ োҳػ AIо ੜ উغ؍ ਬ ೞ ա۽ ো מ۱ ࠗ ঠӝ ؽ. - 1970֙ AI ѹ - 1980֙ AI ѹ - 2012֙ীঠ AIо ࡄਸ ࠆ. ݠन۞ب GPUо פ.
NVIDIA ୡഋ GPU (2রо)
GOOGLE ݠन۞ ਊ TPU (TENSOR PROCESSING UNIT)
־о աਃ?
None
None
࠙ܨ ޙઁܳ ۽Ӓې߁ ਵ۽ ೧Ѿೡ ࣻ হաਃ?
None
- ೦࢚ ৻о ਸ ࣻ णפ. (Ҋন৬ ъই ࠙ܨب ۽Ӓې߁
য۰) - ੋр যח ݽഐೞҊ ܻо ਗೞח Ѿҗܳ ӝ য۰ ࣻ णפ. - ৻о ࢤѹب ৻ੋ ؘఠب ӝ҅ णਸ दఃݶ ೧Ѿؾפ. - ݠन ۞ ѾҴ ܻо ҙਵ۽ ܖ؍ ࠗ࠙ਸ ೧Ѿ೧ સפ. ҙ ঌҊ્ܻਵ۽ ಽӝ য۵णפ
- য়ܲଃ ޙઁח ࢶਸ Ӓযࢲ ޙઁܳ ೧Ѿೡ ࣻ হ. -
ࠂೠ ޙઁח க ۨযо ਃ. ੑ۱கҗ ۱கਵ۽ח উغח ޙઁ
X1 X2 X784 … … ੑ۱க Y1 Y2 Y10 …
۱க ץக - ࠂೠ ޙઁח 1ѐ ࢚ ץக(Hidden Layer)ਸ ٟ݅פ. - 2ѐ ࢚ ץகਸ णೞח ҃ Deep LearningۄҊ ೞݴ बக न҃ݎ (Deep Neural Network)ۄח ݺடਸ ࢎਊפ. - Microsoftח 152கਸ о ResNetਸ ٜ݅ റ 1001ѐ கө ٜ݅णפ.
MNIST पઁ ٘ ؘݽ
CNN
Ҋন VS ѐ - बகݎਵ۽ח ࠙ܨ ޙઁܳ ࠂೞӝ য۰ਛणפ.
- CNN (Convolutional Neural Network) AlexNet աয়ݶࢲ ޙઁܳ ೧Ѿ. - 2012֙ 9ਘ 30ੌࠗఠ ஹೊఠо ҙ ਸ ֈযࢲӝ द೮णפ.
0 1 2 3 4 5 6 7 8 KERNEL
೨ब ੑ۱ 0 1 2 3 ழօ 19 25 37 43 X = - 0 x 0 + 1 x 1 + 3 x 2 + 4 + 3 = 19 - “ழօ” “ ఠ”ۄҊ ࢤпೞݶ ؾפ. ఠ۽ ؘఠܳ оҕೞח Ѫ. - ఠ݃ ౠࢿਸ ъചೠҊ ࢤпೞҊ णפ. - যڃ ఠח ਮҘਸ ъച? - যڃ ఠח ӈա ܳ ؊ ੜ ࠁѱ ъച? э ࢚࢝ՙܻ ғೞҊ ؊פ.
X1 X2 X784 … … ੑ۱க Y1 Y2 Y10 …
۱க ץக ழօ 3ѐ ఠ
CNNਸ ా೧ റࠁܳ ҳೞҊ ހప ܳ۽ ܻ ࢲ۽ ࣁࠗੋ ࣻܳ
੍णפ.
CNN पઁ ٘ ؘݽ
ъചण ъചण
None
0ࠗఠ 8ө ਸ ࢚కۄҊ ࢤпद. ࢚ೞઝ ೞաܳ Ҋܰח Ѫਸ
ঘ࣌ۄҊ ࠁҊਃ.
ъചण - ഒ ۨ (Self Play)ܳ ೞݴ प۱ਸ ט۰х. -
(࢚క, ঘ࣌, थಁ ৈࠗ)ܳ оҊ ण, റ ࢚కী ೧ ࣻ೯೧ঠ ೡ ঘ࣌ਸ ঌ۰ષ. - ঌҊח झझ۽ ؊ ъ೧. (ࢎ णೠ ӝࠁח ӂ ೧Ѿػ 16݅ ӝࠁ. ࠗ࠙ അ ۽о ইש.) - CNN (଼ݎ, оݎ) + ހప ܳ۽ ܻ ࢲ + ъച ण - ঌ ઁ۽ח ӝࠁ णب ೞ ঋҊ ъച ण݅ਵ۽ ֎ਕܳ ҳ୷. X1 X2 X784 … … ࢚క Y1 Y2 Y10 … ঘ࣌ ץக ழօ
MINIMAX۽ ೧ب ؾפ. ъചण + न҃ݎ + ހపܳ۽ ܻ ࢲ۽
ٜ݅णפ. ALPHAZEROی э ߑधਵ۽ਃ.
ALPHA ZERO ۿ ؘݽ
ੋҕמ ࢎਊ ܻ࠭ ಣо ߣӝ ࢤࢿ न҃ݎ ਯ
೯ ର GPT-3 न࠙ૐ ੋध ୁࠈ नਊಣоݽ؛
- “ঘ࣌ ഴܯೞҊ ࠺ ഴܯ೮णפ.” -> ଵ (୶ୌ) - “ցޖ
ਤҊ Ѣܻо ࡞೮णפ.” -> Ѣ (࠺୶) - CNNਵ۽ णदெ ࢜۽ ܻ࠭ী ೧ ୶ୌੋ ࠺୶ੋ ഛੋ. ܻ࠭ ಣо
- RNNਸ ࢎਊ. CNNҗ ର ץக ؘఠܳ द ץகਵ۽ (ӝর۱)
- ҳӖ न҃ݎ ӝ߈ ߣӝܳ ݅ٚ റ ֎ߡ Ҋ ١ ࠗ࠙ न҃ݎਵ۽. - ҳӖ য ܳ Ҋਊೞ ঋחҊ ೣ. ߣӝ RNN Y X RNN I RNN LOVE RNN YOU RNN դ RNN օ RNN ࢎی೧
- GAN (Generative Adversarial Network) - ف ࢎۈ ઓೞ
ঋ ࢎۈ. ࢤࢿ न҃ݎ
CNNਸ ਊ೧ࢲ о ܳ ٜ݅য ղח GENERATOR৬ о ܳ ౸ݺೞח
DISCRIMINATORо ݀ೣ. थܫ 50%о ؼ ٸө ߈ࠂೞݶ о ࢤࢿ.
IMAGE COLORING & IMAGE NOISE REDUCTION
None
ਯ ೯ ର
None
- openAIо ݅ٚ ੋҕמ. - Generative Pre-trained Transformer 3 -
ߣҗ ച, ޙ оמ - ࠺ب ण - “I love you so much”ী ೧ ਵ۽ ण. - I -> Love - I love -> you - I love you -> so - I love you so -> much GPT-3
GPT-2о ೠҴয ؘݽо যࢲ ੋਊ. SKTо ݅ٚ KOGPT2
- न࠙ૐ ղਊ ੋध. - о न࠙ૐੋח Ѩૐ೧ঠೣ. - CNNਸ
ਊ. न࠙ૐ ੋध
- ࢚ਗ ച ࢸ҅. ୁࠈ റ पઁ ࢎۈ .
- ୡӝ ୁࠈ RNNਵ۽ ҳഅ - బஎ, ण, QA ਵ۽ . - য়ח 89.7%, য়ߛח 34.1% ࢚ ୁࠈਵ۽ ৮ܐ. - ੌ߈೯ 10% بо ୁࠈ . ୁࠈ
- ਬ ੑ۱ ղਸ ࠙ࢳ೧ࢲ زਵ۽ Ә. Әా ز ݽਵӝ
- ৈ۞ Ҋё ؘఠܳ ਊ೧ ݠन۞ਸ ਊ. - नਊಣоܳ ా೧
नਊ. नਊಣоݽഋ
хࢎפ.