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
150
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
650
MVC부터 MVVM, 단방향 데이터 흐름까지
dalinaum
5
910
Other Decks in Technology
See All in Technology
GraphRAG グラフDBを使ったLLM生成(自作漫画DBを用いた具体例を用いて)
seaturt1e
1
150
What's new in OpenShift 4.20
redhatlivestreaming
0
290
オブザーバビリティが育むシステム理解と好奇心
maruloop
2
1.3k
Open Table Format (OTF) が必要になった背景とその機能 (2025.10.28)
simosako
2
340
OTEPsで知るOpenTelemetryの未来 / Observability Conference Tokyo 2025
arthur1
0
270
AI AgentをLangflowでサクッと作って、1日働かせてみた!
yano13
1
160
会社を支える Pythonという言語戦略 ~なぜPythonを主要言語にしているのか?~
curekoshimizu
3
840
AWS DMS で SQL Server を移行してみた/aws-dms-sql-server-migration
emiki
0
250
Behind Postgres 18: The People, the Code, & the Invisible Work | Claire Giordano | PGConfEU 2025
clairegiordano
0
140
混合雲環境整合異質工作流程工具運行關鍵業務 Job 的經驗分享
yaosiang
0
190
オブザーバビリティと育てた ID管理・認証認可基盤の歩み / The Journey of an ID Management, Authentication, and Authorization Platform Nurtured with Observability
kaminashi
1
740
Kubernetes self-healing of your workload
hwchiu
0
560
Featured
See All Featured
Stop Working from a Prison Cell
hatefulcrawdad
272
21k
Keith and Marios Guide to Fast Websites
keithpitt
411
23k
The World Runs on Bad Software
bkeepers
PRO
72
11k
Automating Front-end Workflow
addyosmani
1371
200k
Leading Effective Engineering Teams in the AI Era
addyosmani
7
640
What's in a price? How to price your products and services
michaelherold
246
12k
The Art of Programming - Codeland 2020
erikaheidi
56
14k
Building Adaptive Systems
keathley
44
2.8k
Designing Experiences People Love
moore
142
24k
実際に使うSQLの書き方 徹底解説 / pgcon21j-tutorial
soudai
PRO
190
55k
The Success of Rails: Ensuring Growth for the Next 100 Years
eileencodes
46
7.7k
Evolution of real-time – Irina Nazarova, EuRuKo, 2024
irinanazarova
9
1k
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.