Upgrade to Pro
— share decks privately, control downloads, hide ads and more …
Speaker Deck
Features
Speaker Deck
PRO
Sign in
Sign up for free
Search
Search
AlphaGo에서 시작하는 인공지능
Search
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
Tweet
Share
More Decks by Leonardo YongUk Kim
See All by Leonardo YongUk Kim
Recap: Kotlin Language Features in 2.0 and Beyond (Michail Zarečenskij)
dalinaum
0
660
Compose Multiplatform 101
dalinaum
3
670
Kotlin 2.0을 통해 알아보는 코틀린의 미래
dalinaum
1
2.5k
실리콘밸리 스타트업에서 일어난 일
dalinaum
0
140
Zip: Data compression (20분만에 배우는 압축 알고리즘)
dalinaum
1
2.6k
안드로이드 빌드: 설탕없는 세계
dalinaum
0
150
Obfuscation 101 @ Naver Tech Concert
dalinaum
4
620
Realm은 어떻게 효율적인 데이터베이스를 만들었나?
dalinaum
1
640
MVC부터 MVVM, 단방향 데이터 흐름까지
dalinaum
5
900
Other Decks in Technology
See All in Technology
神回のメカニズムと再現方法/Mechanisms and Playbook for Kamikai scrumat2025
moriyuya
4
330
stupid jj tricks
indirect
0
7.8k
実装で解き明かす並行処理の歴史
zozotech
PRO
1
280
Go Conference 2025: GoのinterfaceとGenericsの内部構造と進化 / Go type system internals
ryokotmng
3
610
非エンジニアのあなたもできる&もうやってる!コンテキストエンジニアリング
findy_eventslides
3
880
Goを使ってTDDを体験しよう!
chiroruxx
1
260
VCC 2025 Write-up
bata_24
0
150
Oracle Cloud Infrastructure:2025年9月度サービス・アップデート
oracle4engineer
PRO
0
370
いまさら聞けない ABテスト入門
skmr2348
1
190
タスクって今どうなってるの?3.14の新機能 asyncio ps と pstree でasyncioのデバッグを (PyCon JP 2025)
jrfk
1
230
"複雑なデータ処理 × 静的サイト" を両立させる、楽をするRails運用 / A low-effort Rails workflow that combines “Complex Data Processing × Static Sites”
hogelog
3
1.7k
Railsアプリケーション開発者のためのブックガイド
takahashim
14
6k
Featured
See All Featured
Reflections from 52 weeks, 52 projects
jeffersonlam
352
21k
Chrome DevTools: State of the Union 2024 - Debugging React & Beyond
addyosmani
7
890
Statistics for Hackers
jakevdp
799
220k
I Don’t Have Time: Getting Over the Fear to Launch Your Podcast
jcasabona
33
2.4k
Facilitating Awesome Meetings
lara
56
6.6k
The Cost Of JavaScript in 2023
addyosmani
53
9k
個人開発の失敗を避けるイケてる考え方 / tips for indie hackers
panda_program
114
20k
Optimising Largest Contentful Paint
csswizardry
37
3.4k
How to Create Impact in a Changing Tech Landscape [PerfNow 2023]
tammyeverts
54
3k
Raft: Consensus for Rubyists
vanstee
139
7.1k
The Web Performance Landscape in 2024 [PerfNow 2024]
tammyeverts
9
840
CoffeeScript is Beautiful & I Never Want to Write Plain JavaScript Again
sstephenson
162
15k
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.