이탈리아의 도시에 대해서는 알파고 (이탈리아) 문서를 참고하십시오.
 외신 기자 알파고에 대해서는 알파고 시나씨 문서를 참고하십시오.

**알파고**(영어: AlphaGo)는 구글의 딥마인드가 개발한 인공지능 바둑 프로그램이다.

영국의 스타트업 기업이었던 딥마인드가 2014년 구글에 인수되면서 개발이 본격적으로 진행되었다. 2015~2017년 프로토타입 버전인 알파고 판, 알파고 리, 알파고 마스터가 공개되었고, 2017년 10월에 최종 버전인 알파고 제로를 발표하였다. 2018년 12월에는 바둑을 포함한 보드게임에 적용할 수 있는 범용 인공지능 알파 제로(Alpha Zero)를 발표하였다.

알파고는 2015년 10월 유럽 바둑 챔피언십 (EGC)에서 3차례 우승한 프랑스의 판 후이(Fan Hui, 樊麾) 2단과의 5번기에서 모두 승리해 핸디캡(접바둑) 없이 호선(맞바둑)으로 프로 바둑 기사를 이긴 최초의 컴퓨터 바둑 프로그램이 되었다. 2016년 3월에는 여러 국제 기전에서 18차례 우승했던 세계 최상위급 프로 기사인 이세돌 9단과의 5번기 공개 대국에서 대부분의 예상을 깨고 4승 1패로 승리해 '현존 최고 인공지능'으로 등극하면서 세계를 놀라게 했다. 2017년 5월에는 당시 바둑 세계 랭킹 1위 프로 기사였던 커제(柯洁) 9단과의 3번기 공개 대국과 중국 대표 5인과의 상담기(相談棋, 단체전)에서도 모두 승리하며 '세계에서 가장 강력한 인공지능'임을 다시 한번 각인시켰다. 한국기원은 알파고가 정상의 프로기사 실력인 '입신'(入神)의 경지에 올랐다고 인정하여 '프로 명예 단증(9단)'을 수여하였고, 중국기원도 '프로기사 9단' 칭호를 부여했다. 이 대국을 통해 인공지능의 새 장을 열었다는 평가를 받았으며, 바둑계는 기존의 통념을 깨뜨리는 창의적인 수와 대세관으로 수 천년 동안 이어진 패러다임이 바뀔 것으로 전망했다.

데미스 허사비스(Demis Hassabis) 최고경영자(CEO)는 2017년 5월에 열린 '바둑의 미래 서밋(Future of Go Summit)'이 알파고가 참가하는 마지막 대회가 될 것이며, 앞으로 인공지능은 인류가 새로운 지시영역을 개척하고 진리를 발견할 수 있도록 돕게 될 것이라고 말했다. 딥마인드는 질병진단 및 건강관리, 신약개발, 기후변화예측, 무인자율주행차, 스마트폰 개인비서 등 사회 전분야로 확대해 미래의 다양한 핵심 서비스 사업에 적용할 수 있는 범용 인공지능으로 개발한다는 계획이다.

알파고라는 이름은 구글의 지주회사 이름인 알파벳과 그리스 문자의 첫 번째 글자로 최고를 의미하는 '알파(α)', 바둑의 일본어 발음 '碁(ご)'에서 유래한 영어 단어 'Go'를 뜻한다. 통산 전적은 73승 1패이다.

알파고 이전의 개발 역사와 대전
-----------------

### 인간 대 바둑 프로그램

바둑은 체스와 같은 다른 종목에 비해 컴퓨터가 인간을 이기기 훨씬 어려운 것으로 여겨졌다. 체스 등보다 가능한 국면의 수가 훨씬 크기 때문에, 브루트 포스 등 전통적인 인공지능 기법 적용이 매우 곤란하기 때문이다.

1997년 IBM의 컴퓨터 딥 블루가 세계 체스 챔피언 가리 카스파로프(Garry Kasparov)를 상대로 승리한 이후 20여년 동안 바둑 프로그램의 인공지능은 인간 아마추어 기사 5단의 수준까지 도달했지만, 여전히 핸디캡 없이 프로 바둑 기사를 이길 수 없었다. 2012년, 4대의 PC 클러스터로 운용되는 프로그램 젠(Zen)은 프로 기사 다케미야 마사키(武宮正樹) 9단과의 4점 접바둑으로 5전 2승을 거두었고, 프랑스에서 개발된 크레이지 스톤(Crazy Stone)은 2013년, 이시다 요시오(石田芳夫) 9단과의 4점 접바둑에서 이겼다.

알고리즘
----

인공지능의 학습에 있어 데이터와 연산능력 보다 중요시 되는 것이 알고리즘이다. 핵심은 무한대에 가까운 광범위한 경우의 수를 줄이는 것이다. 알파고는 훈련된 심층신경망(DNN, Deep Neural Network)이 몬테카를로 트리 탐색(MCTS, Monte Carlo Tree Search) 통해 선택지 중 가장 유리한 선택을 하도록 설계되었다. 심층신경망은 정책망(policy network)과 가치망(value network)의 결합에 의해 이루어진다. 정책망은 승리 가능성이 높은 다음 수를 예측하여 검색 범위를 좁히고, 가치망은 트리 탐색의 단계(depth)를 줄여 끝날 때까지 승률을 계산하여 승자를 추정한다. 이를 실현하기 위한 기계학습은 여러 계층(layer)으로 디자인된 정책망을 구성하고, 정책망 지도학습, 정책망 강화학습, 가치망 강화학습 단계를 거친다.

###### 정책망 지도학습 (Supervised learning of policy networks)

KGS 바둑 서버에 등록된 16만 개의 기보를 회선신경망(CNN, Convolutional Neural Networks)으로 학습하고 3천 만개 이르는 착점 위치 정보와 패턴을 파악해 다음 수를 예측하여 인간의 바둑을 흉내내도록 훈련되었다. 훈련 결과 기존 44% 수준의 예측 확률이 57%까지 높아졌다.

###### 정책망 강화학습 (Reinforcement learning of policy networks)

기보에만 최적화 되는 한계를 극복하기 위해 반복적인 자가 대국으로 정책망의 성능을 개선한다. 무작위로 선정된 신경망 사이의 자가 대국을 통해 학습하며, 승리하면 보상을 받고(+1) 패하면 보상을 잃는(-1) 방식으로 진행한다. 이 과정을 거쳐 강화학습 이전의 정책망과 비교해 80% 더 많은 대국에서 이길 수 있게 되었다.

###### 가치망 강화학습 (Reinforcement learning of value networks)

결과 예측을 강화하는 단계로 정책망의 자가 대국으로 확보된 기보를 바탕으로 승률을 파악하고 가중치를 부여해 다음 대국을 진행하는 방식으로 가치망의 분석 능력을 향상시킨다.

2016년 이세돌 9단과 대국한 알파고는 12개의 신경망 계층을 활용해 지도학습과 이를 통해 가장 합리적인 수를 도출하는 강화학습을 병행하였고, 2017년 커제 9단과 대국한 알파고 마스터는 신경망 계층을 40개로 늘려 지도학습 없이 강화학습만으로도 기력을 향상시킬 수 있도록 진화했다. 딥 블루는 특정 목적을 위해 만들어진 인공지능이라는 한계가 있었지만, 알파고의 알고리즘은 여러 분야에 범용으로 활용할 수 있다는 강점이 있다.

딥마인드는 2016년 영국의 국민건강보험공단(NHS)와 협약을 맺고 알파고의 인공지능 알고리즘을 활용한 딥마인드 헬스(DeepMind Health)를 개발하여, 환자 치료와 진단 속도를 단축하는 기술을 시험하고 있다. 실제로 병원 의료진들이 매일 2시간 정도 절약하는 효과가 있는 것으로 알려졌다.

하드웨어
----

### 2015년의 알파고

알파고는 단일 컴퓨터로 구동되는 '단일 버전(Single version)'과 네트워크에 연결된 여러 대의 컴퓨터를 사용하는 '분산 버전(Distributed version)' 두 가지가 있다. 단일 버전의 알파고는 48개의 CPU와 4~8개의 GPU로 구동되며 '크레이지 스톤'과 '젠'을 포함한 다른 바둑 프로그램과 500번의 대국에서 1패만 기록하였다. 분산 버전은 1,202~1,920개의 CPU와 176~280개의 GPU로 구성되어 있다. 다양한 수의 CPU와 GPU에서 비동기 모드와 분산 모드로 테스트되었고. 한 수당 생각할 시간은 2초씩 배정되었다. 엘로 평점(ELO rating)은 아래와 같다.

배열 및 성능| 배열 | 검색 쓰레드 | CPU 수 | GPU 수 | 엘로 평점 |
| --- | --- | --- | --- | --- |
| 비동기 | 40 | 48 | 1 | 2,151 |
| 비동기 | 40 | 48 | 2 | 2,738 |
| 비동기 | 40 | 48 | 4 | 2,850 |
| 비동기 | 40 | 48 | 8 | 2,890 |
| 분산 | 12 | 428 | 64 | 2,937 |
| 분산 | 24 | 764 | 112 | 3,079 |
| 분산 | 40 | 1,202 | 176 | 3,140 |
| 분산 | 64 | 1,920 | 280 | 3,168 |

### 알파고 판 (AlphaGo Fan)

176개의 GPU가 사용된 분산 버전이다. 2015년 판 후이 2단과 대국에서 승리하였다. 당시에는 '버전12'로 알려졌다.

### 알파고 리 (AlphaGo Lee)

48개의 TPU가 사용된 분산 버전이다. 2016년 3월 이세돌 9단과 대국에서 승리하였다.

대국 당시에는 알파고 판에서 기계학습이 개선된 수준의 '버전18'이라는 정도만 알려졌었다. 하지만 2016년 5월 구글 I/O(개발자회의) 2016 컨퍼런스에서 구글은 자체 개발한 애플리케이션 집적회로(ASIC)인 TPU(Tensor Processing Unit)를 처음 소개하면서 이세돌 9단과 대국한 알파고는 TPU가 사용되었다고 설명했다.

### 알파고 마스터 (AlphaGo Master)

4개의 TPU가 사용된 단일 버전이다. 2017년 초 프로 바둑기사와 온라인 대국에서 60연승을 하였고, 같은 해 5월 커제 9단과의 대결에서도 승리하였다.

2세대 TPU 모듈 1개가 탑재된 '1대의 TPU 머신'으로 구동되는 알파고 마스터는 커제 9단과 대국 이전이었던 2017년 5월 17일 구글 I/O 2017 컨퍼런스에서 공개되었다. 여기에 사용된 TPU 모듈은 연산성능 45TFLOPS(테라플룹스, 1초에 45조번의 연산처리)짜리 TPU 4개로 구성되어 180TFLOPS 성능을 내며, 1개의 모듈은 64기가바이트(GB) 메모리 대역폭을 지원한다. 구글은 TPU의 연산 성능은 당시의 최신 CPU보다 30~80배 높다고 설명하였다.

단일 버전인 알파고 마스터의 연산 능력은 분산 버전의 10% 수준이지만, 주 기능을 인공지능 연산과 예측에만 특화해 기계학습(machine learning) 알고리즘과 텐서플로(TensorFlow), 프레임워크(framework) 구동에 최적화되었다. 기존의 알파고가 학습한 내용을 토대로 추론했다면 알파고 마스터는 추론과 동시에 학습할 수 있고, 학습에 필요한 시간이 기존의 3분의1로 단축되었다. 또한 머신의 물리적인 부피가 줄어들면서 에너지 효율은 10배가량 향상되었다.

### 알파고 제로 (AlphaGo Zero)

4개의 TPU가 사용된 단일 버전으로 알파고의 최종 버전이다. 2017년 10월 19일 과학 학술지 네이처에 ‘인간 지식 없이 바둑을 마스터하기(Mastering the game of Go without human knowledge)’라는 제목의 논문 발표를 통해 소개되었다.

알파고 제로는 인간의 기보에 의존하는 지도학습 없이 바둑 규칙만으로 스스로 학습하며 기력을 향상시킨다. 학습 36시간 만에 알파고 리의 수준을 능가하였고, 72시간 만에 알파고 리와 대국에서 100승 하는 동안 패하지 않았으며, 40일 후 알파고 마스터와 대국에서는 89승 11패를 기록하였다. 이 기간 동안 알파고 제로는 2900만 번의 자가 대국을 진행하며 학습하였다. 빅데이터 학습이 필요없는 인공지능의 등장은 바둑과 달리 빅데이터 확보가 어려워 인공지능을 활용하기 어려웠던 분야에 해결책을 제시했다는 점에서 의미가 있다.

### 알파 제로 (Alpha Zero)

하나의 알고리즘으로 바둑, 체스, 쇼기 등의 보드게임에 적용되는 범용 인공지능이다. 2018년 12월 7일 ‘자가학습을 통해 체스, 쇼기, 바둑을 마스터할 수 있는 범용 강화학습 알고리즘(A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play)’이라는 제목의 논문을 과학 학술지 사이언스에 발표했다.

알파고 제로와 마찬가지로 빅데이터 학습이 필요 없을 뿐만 아니라, 점점 강해지는 자신과의 게임을 반복하면서 스스로 빅데이터를 구축한다. 2016년 쇼기 대회에서 우승한 AI '엘모(Elmo)'와의 대결에서 승률에서 앞서기까지 2시간, 2017년 체스 챔피언을 차지한 AI '스톡피시(Stockfish)‘는 4시간, 알파고 제로는 30시간이 걸렸다. 데미스 허사비스 CEO는 "현실 세계의 문제를 푸는 인공지능을 만들고자 하는 딥마인드의 최종 목표에 다가서는 중요한 여정"이라고 밝혔다.

프로 바둑 기사와의 대국
-------------

### 판 후이 2단과의 대국 (2015)

2015년 10월, 분산 버전의 알파고(버전12)는 2013~2015년 유럽 바둑 챔피언쉽 우승자인 프로 기사 판 후이 2단과의 5번기에서 모두 승리했다. 이는 인공지능이 19×19 바둑판 위의 대국에서 프로 기사를 상대로 한 최초의 승리였다. 비공개로 진행되었던 이 대국의 결과는 알고리즘을 설명한 관련 논문이 실린 과학 학술지 네이처의 출판일에 맞추어 지연 발표되면서 2016년 1월 27일 이후 알려졌다.

판 후이 2단과의 대국은 제한시간 1시간, 초읽기 30초 3회, 7집 반 덤 중국 규칙을 적용하며 진행되었다.

#### 대국 예

* 2015년 10월 8일 - 알파고(흑), 판 후이 2단(백), 흑 불계승.

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| 100~165수 |

### 구글 딥마인드 챌린지 매치 - 이세돌 9단과의 대국 (2016)

 이 부분의 본문은 알파고 대 이세돌입니다.
구글은 대한민국의 프로 기사 이세돌 9단과 대국을 성사시켜 2016년 3월 9일부터 15일까지 서울의 포 시즌스 호텔에서 구글 딥마인드 챌린지 매치(Google Deepmind Challenge match)를 개최하였다. 인간과 인공지능의 대결, 문화와 과학의 대결 등으로 수많은 화제 불러일으키며 역사적인 세기의 대결로 주목받은 첫 대국은 한국어와 영어로 공식해설이 제공된 유튜브(YouTube) 생중계를 통해 전 세계 8천 만명 이상이 시청하였으며, 대한민국에서는 한국방송공사(KBS2)에서 생중계되었다.

이후 이세돌 9단과의 대국 과정과 뒷이야기 등을 흥미있게 다룬 다큐멘터리 영화 《알파고》가 제작되었다. 이 영화는 2017년 4월 트라이베카 영화제에서 처음 상영되었고, 제43회 서울독립영화제에서도 상영되었다.

#### 대국 방식

5전 3선승제이지만 보통 일반 기전과 달리 승패에 상관없이 5국까지 모두 진행하였다. 알파고는 영국 런던의 딥마인드 본사에서 운용하였고, 미국에 중서부에 위치한 구글의 클라우드 서버에서 구동되었다. 구글의 연구개발자이자 아마추어 6단인 아자 황(Aja Hwang, 黃士傑) 박사가 알파고를 대신하여 바둑판에 돌을 놓고, 이세돌 9단이 착점한 수를 컴퓨터에 입력하는 방식으로 진행되었다. 중국 규칙을 따라 7점 반의 덤을 적용하여 공제하고, 양측은 제한시간 2시간씩, 초읽기는 60초 3회가 주어졌다.

#### 대국 결과

* 3월 9일 제1국 186수, 백 불계승
* 3월 10일 제2국 211수, 흑 불계승
* 3월 12일 제3국 176수, 백 불계승
* 3월 13일 제4국 180수, 흑 불계패
* 3월 15일 제5국 280수, 백 불계승

알파고는 치밀하고 정교한 수 읽기와 정확한 집계산 능력을 과시하며 1~3국을 연속으로 승리해 우승을 조기에 확정지었다. 이세돌 9단은 4국에서 승리한 후 기자회견에서 "알파고가 노출시킨 약점은 두 가지다. 첫 번째 알파고는 백보다 흑을 힘들어했다. 두 번째는 자기가 생각하지 못했던 수가 나오면 버그 형태로 몇 수를 진행하는 것을 보았다. 알파고는 생각을 못했을 경우 대처 능력이 떨어진다."라고 지적하였고, 데미스 허사비스 CEO는 "신경망은 스스로 바둑을 두며 학습하도록 돼 있기 때문에 지식의 공백이 있을 수 밖에 없다. 이세돌 9단과의 대국을 통해 이런 한계를 알 수 있었다."고 설명하면서 패배를 통해 알파고의 한계를 알게 된 것을 큰 성과로 꼽았다. 딥마인드는 미화 100만 달러의 우승 상금은 유니세프와 STEM(과학 · 기술 · 공학 · 수학) 교육 및 바둑 관련 자선단체에 기부한다고 밝혔다.

#### 대국 예

* 2016년 3월 10일의 2국 - 알파고(흑) 대 이세돌(백), 흑 불계승

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| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
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|

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| 1수~99수 |

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|

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| 100수~199수 |

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| 

|  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
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|

 |
| 200수~211수 |

* 2016년 3월 13일의 4국 - 알파고(흑) 대 이세돌(백), 백 불계승. (78수 승착)

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| 

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| 1수~99수 |

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| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
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| 100수~180수 (177 자리에 , 178 자리에 ) |

### 프로 기사와의 온라인 대국에서 60연승 (2017)

한국의 온라인 바둑전문 사이트인 타이젬에 등장한 Magister라는 아이디는 2016년 12월 29일부터 31일까지 30전 30승을 기록하였고, 중국의 텐센트가 서비스 중인 한큐바둑에 등장한 Master라는 아이디도 2017년 1월 2일부터 4일까지 30전 30승을 기록하면서 바둑계는 비상한 관심을 보였다. 연승에 놀란 한 중국 기사는 10만 위안의 현상금을 걸기도 했는데, Magister와 Master는 밤낮없이 대국을 하면서도 대부분 170수 근처에서 불계승으로 끝내는 등 압도적인 기력을 선보여 사람이 아니라 동일한 인공지능 즉 알파고라는 추측이 나돌았다. 2017년 1월 4일 데미스 허사비스 CEO는 트위터를 통해 수수께끼의 바둑계정 Magister와 Master는 알파고의 새로운 버전이라고 밝혔다.

이 온라인 대국에서 당시 한국 랭킹 1위 박정환 9단이 4패, 세계 랭킹 1위 커제도 3패를 기록했고, 일본 랭킹 1위인 이야마 유타(井山裕太) 9단도 패하는 등 한중일의 최정상급 프로 바둑 기사들이 잇달아 패하였다. 이세돌 9단은 '초읽기 상황에서 다소 약점을 보였으나 이 부분이 보완됐다'고 분석했고, 안성준 7단은 '경이로울 정도로 강하며 인간이 갖고 있는 고정관념이 전혀 없다'고 분석하면서 '알파고를 통해 새로운 바둑 세계의 탐험이 가능해졌다'는 점에서 긍정적으로 평가했다.

#### 대국 예

* 2016년 12월 31일의 온라인 대국 - 알파고(백) 대 탕웨이싱 9단(흑), 백 불계승

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| 1~99수 |

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### 바둑의 미래 서밋 - 커제 9단과의 대국 (2017)

 이 부분의 본문은 알파고 대 커제입니다.
딥마인드는 2017년 4월 10일 홈페이지를 통해 알파고와 중국의 정상급 프로 기사들이 참여하는 '바둑의 미래 서밋’(Future of Go Summit)'을 중국 저장(浙江)성 우전(烏鎭)의 인터넷 국제컨벤션센터에서 개최한다고 밝혔다. 중국기원도 같은 날 기자회견을 열어 2017년 5월 23일부터 27일까지 알파고와 대국 일정을 발표했다. 이 대회는 당시 세계 바둑 랭킹 1위였던 중국의 커제 9단과 알파고의 3번기 대국 외에도 스웨(時越) 9단, 천야오예(陳耀燁) 9단, 미위팅(芈昱廷) 9단, 탕웨이싱(唐韦星) 9단, 저우루이양(周睿羊) 9단이 중국 대표로 한팀을 이뤄 알파고와 대국하는 상담기와 '구리(古力) 9단-알파고'팀과 '롄샤오(連笑) 8단-알파고'팀이 대국하는 페어바둑(복식전)도 열렸다. 이세돌 9단과의 대국과 마찬기지로 중국 규칙인 7점 반의 덤을 공제하고, 커제 9단과의 대국은 제한시간은 3시간씩, 초읽기는 60초 3회가 주어졌으며, 상담기와 페어바둑은 초읽기는 같고 제한시간은 2시간 30분이 주어졌다. 우승상금은 미화 150만 달러(약 17억원), 1국의 대국료는 10만 달러로 책정되었다.

* 5월 23일 제1국 289수, 백 한집반승
* 5월 25일 제2국 115수, 흑 불계승
* 5월 26일 페어대국 220수, '렌샤오-알파고B'팀 백 불계승
* 5월 26일 상담기대국 254수, 백 불계승
* 5월 27일 제3국 209수, 흑 불계승

커제 9단은 3번의 대국에서 조금씩 다른 바둑을 선보이며 분전했지만 알파고의 벽을 넘지 못하고 모두 패하였다. 중국 대표 5인과의 상담기 역시 알파고가 승리했다. 이 대국 이후 바둑계 안팎에선 인간의 수준을 넘어선 알파고의 바둑을 평가하는 건 큰 의미가 없다는게 중론이다.

한편 딥마인드는 알파고가 이세돌 9단과 대국 이후 강화학습을 위해 스스로 대국했던 '셀프 대국' 기보 50개를 홈페이지 Archived 2019년 8월 5일 - 웨이백 머신를 통해 대회 마지막날부터 순차적으로 공개하였다. 데미스 허사비스 CEO는 이번 대회가 알파고가 참가하는 마지막 경기가 될 것이라고 전하면서, 알파고가 두었던 수를 기초로 '바둑 지도 도구'를 개발하고 알파고가 이세돌 9단과 대국 이후 업그레이드된 진화 과정을 논문으로 발표할 계획도 밝혔다.

기풍, 기력
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대체로 '상대가 누구든 이길 수 있는 만큼만 둔다'라는게 공통적인 견해다. 알파고는 응수타진이나 사석작전 같은 소위 '인간적인' 전략을 능수능란하게 구사한다는 점에서 이전의 바둑 프로그램과 차별화 된다. 김명완 9단은 판 후이 2단과의 대국에서 알파고는 '사람 같다'고 묘사하였고, 판 후이 2단 과의 대국에서 심판이었던 토비 매닝은 프로그램의 방식을 '보수적'이라고 표현했다. 김성룡 9단은 이세돌 9단과의 5국을 해설하면서 "이창호 9단이 새로운 기풍으로 바둑계를 놀라게 했던 것과 비슷한 느낌이다. 계산의 신(神算)으로 불렸던 이창호 9단의 전성기와 닮아있다"라고 설명하였다.

2017년 커제 9단과 대국한 알파고는 단순한 계산이나 수 읽기 뿐만 아니라 승률을 기반으로 초반부터 포석을 강하게 압박해가며 넓은 시야로 전체 판을 보는 것이 최대 강점이며, 2016년 이세돌 9단과의 대국 때보다 월등하게 진화된 모습으로 상대를 압도했다는 평가다. 목진석 9단도 '형세을 읽는 판단이 뛰어났고, 부분적으로 손해를 보는 것 같지만 전체 형세는 더 좋았다'고 설명하였다. 커제 9단과의 3국 해설을 진행한 이세돌 9단은 '자신과 대국했던 알파고는 이상한 수를 두기도 했는데, 특별한 실수 없이 가장 안정적인 수를 찾아갔다'면서 '난전으로 끌고 가되 나빠지지 않게 해야하는데 어려운 문제'라고 설명했다. 커제 9단은 첫 대국 후 신의 경지에 가까웠다고 표현했다.

알파고 마스터의 엘로 평점은 4500점대로 추산된다. 세계 바둑 랭킹 10위귄의 프로 기사 점수는 3500~3600점 수준으로, 1000점 차이면 정선 정도의 차이로 여겨진다. 이는 세계 최정상급의 프로 바둑기사도 맞바둑(호선)으로 1000번 대국에서 6~8승 정도로 100번의 대국에서 1승도 어려운 수준이며, 인간은 알파고를 상대로 승리하는 건 불가능한 것으로 평가된다.

사회적 반향
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### 과학계

바둑을 두는 것은 머신 러닝에서 어려운 문제로 평가되어 왔기에 알파고는 인공 지능 연구의 랜드마크적 성장으로 일컬어졌다.

대부분의 전문가들은 알파고와 같은 강력한 바둑 프로그램은 최소한 5년 후에나 가능할 것으로 생각했다. 일부 전문가는 컴퓨터가 바둑 챔피언을 이기려면 최소한 10년은 더 있어야 한다고 생각했다.

2016년 3월에 개최된 알파고와 이세돌의 대결은 인공 지능 연구에 있어 획기적인 사건이었다. 대국 전에 대부분의 참관인들은 이세돌이 알파고를 이길 것으로 기대하였다. 하지만, 5차례의 대국에서 알파고는 이세돌을 4대 1로 이겼다.

체스와 함께 바둑에서도 컴퓨터가 사람을 이기면서 기존의 방식으로 인기 보드 게임에서 이기는 것은 더 이상 인공 지능의 중대 사건이 아니게 되었다. 딥 블루의 머리 캠벨은 알파고의 승리를 '한 시대의 끝 … 보드 게임은 거의 마무리되었고 이제는 옮겨갈 때'라고 하였다.

딥 블루나 왓슨과 비교하여 알파고의 근원적인 알고리즘은 보다 다목적인 잠재성이 있으며, 과학계가 인공 일반 지능(AGI, artificial general intelligence)으로 진전하고 있다는 증거일 수 있다.

일부 해설자들은 알파고의 승리는 사회에 있어 인공 일반 지능을 지닌 기계가 가져올 수 있는 미래의 충격에 대한 대비의 논의를 시작하는 좋은 기회를 가져다 주었다고 평가한다. AI 연구자 스튜어트 러셀은 'AI의 체계성은 예상한 것보다 훨씬 빠르게 진척되고 있어서 장기적 성과에 대한 문제를 더욱 긴급하게 하고 있다'면서, '갈수록 강력해지는 AI 체계가 완전히 인간의 통제하에 남아 있음을 보장하기 위해서 … 할 일이 많다'고 하였다. 스티븐 호킹과 같은 일부 학자들은 미래의 자기 개량 AI는 사실상의 일반 지능을 얻을 수 있으며, 기대하지 않았던 AI 테이크오버(AI의 지구 장악)를 초래할 수 있다고 경고했다. 또 다른 학자들은 이에 동의하지 않으며, AI 전문가 장 가브리엘 가나시아는 '상식과 같은 것들은 … 영원히 복제할 수 없을 것'이라 하였다. 또한, '우리가 왜 공포에 대해 말해야 하는지 모르겠다. 반대로, 이는 건강이나 우주 탐험 같은 여러 분야에서 희망을 높여주고 있다'고 했다. 컴퓨터 과학자 리처드 서튼은 '사람들이 두려워해야 한다고 생각하지 않지만 … 사람들은 주의해야만 한다'고 했다.

### 바둑계

알파고와 판 후이의 경기 심판이었던 토비 매닝과 국제바둑연맹(IGF) 사무국장 이하진은 미래에는 바둑 참가자들은 대국에서 무엇을 잘못했는지를 알고, 기량을 향상시키기 위해 컴퓨터에 조언을 구하게 될 것이라 추측하였다.

2016년 3월의 대국은 전 세계적으로 수백 만 명이 관람하고 분석하였다. 많은 바둑 기사들은 알파고의 정통적이지 않은 바둑을 처음에는 관람자들을 어리둥절하게 만들지만 다 알고 나면 이치에 맞는, 겉보기에 미심쩍은 행보로 묘사하였다. 알파고는 인공지능으로서는 최초로 핸디캡 없이 프로 기사에 이긴 2015년 10월의 경기와 비교하더라도 예상 외로 강해진 듯했다.

대국 전 이세돌은 '질 수도 있다'고 하면서 '바둑의 아름다움, 인간의 아름다움을 컴퓨터가 이해하고 두는 게 아니므로 바둑의 가치는 계속될 것'이라 했다. 이세돌은 3국 후 그의 패배를 인정하면서 '여러 가지로 알파고 능력을 오판한 것이 많았다'고 했고, '분명 약점이 있는 것 같다. 아직 정말 인간에게 메시지를 던질 수 있는 실력은 아니다.'라고 주장했다. 4국의 승리 후에는 이를 '무엇과도 바꿀 수 없는 가치 있는 승리'라고 했다.

중국의 바둑 1위이자 세계 랭킹 1위인 커제는 처음에는 알파고와 대국하여 이길 수 있다고 주장하였으나, 3국까지의 결과를 분석한 후에는 질 가능성이 크다고 언급하였다. 하지만, 이세돌이 1승을 거둔 대국 후에는 이길 수 있다고 믿는다고 했고, 알파고와의 대전을 트위터로 예고하였다.

유사 시스템
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구글의 경쟁자인 페이스북도 인공지능 바둑 시스템 '다크포레스트'(darkforest)를 제작하고 있으며, 마찬가지로 머신 러닝과 트리 순회를 조합한다. 다른 컴퓨터 바둑 프로그램도 강력한 상대이기는 하지만, 2016년 초인 현재까지 인간인 바둑 기사를 상대로 이긴 적은 없다.

### 알파스타

알파고를 스타크래프트 2에 적용한 알파스타 (AlphaStar)가 프로게이머 TLO와 MaNa를 이겼다.