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스튜어트 러셀,피터 노빅 저 / 류광 역
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제이펍
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2021년 08월 25일
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| [1권] PART I 인공지능 CHAPTER 1 소개 3 1.1 인공지능이란 무엇인가? ·············································································· 4 1.2 인공지능의 기반 학문 ··············································································· 10 1.3 인공지능의 역사 ························································································ 25 1.4 인공지능의 현황 ························································································ 38 1.5 인공지능의 위험과 혜택 ············································································ 43 요약 ······································································································ 47 참고문헌 및 역사적 참고사항 ····························································· 49 CHAPTER 2 지능적 에이전트 51 2.1 에이전트와 환경 ························································································ 52 2.2 좋은 행동: 합리성 개념 ············································································ 55 2.3 환경의 본성 ······························································································· 59 2.4 에이전트의 구조 ························································································ 65 요약 ······································································································ 81 참고문헌 및 역사적 참고사항 ····························································· 82 PART II 문제 해결 CHAPTER 3 검색을 통한 문제 해결 87 3.1 문제 해결 에이전트 ··················································································· 88 3.2 문제의 예 ··································································································· 92 3.3 검색 알고리즘들 ························································································ 97 3.4 정보 없는 검색 전략 ··············································································· 104 3.5 정보 있는 검색(발견적 검색) 전략들 ····················································· 114 3.6 발견적 함수 ······························································································ 130 요약 ···································································································· 140 참고문헌 및 역사적 참고사항 ··························································· 141 CHAPTER 4 복잡한 환경의 검색 147 4.1 국소 검색과 최적화 문제 ········································································ 147 4.2 연속 공간의 국소 검색 ············································································ 157 4.3 비결정론적 동작들을 수반한 검색 ·························································· 161 4.4 부분 관측 가능 환경의 검색 ··································································· 167 4.5 온라인 검색 에이전트와 미지 환경 ························································ 177 요약 ···································································································· 185 참고문헌 및 역사적 참고사항 ··························································· 186 CHAPTER 5 대립 검색과 게임 191 5.1 게임 이론 ································································································· 191 5.2 게임의 최적 결정 ····················································································· 194 5.3 발견적 알파베타 트리 검색 ····································································· 203 5.4 몬테카를로 트리 검색 ·············································································· 210 5.5 확률적 게임 ······························································································ 214 5.6 부분 관측 가능 게임 ··············································································· 218 5.7 게임 검색 알고리즘들의 한계 ································································· 224 요약 ···································································································· 226 참고문헌 및 역사적 참고사항 ························································· 227 CHAPTER 6 제약 충족 문제 235 6.1 제약 충족 문제의 정의 ············································································ 236 6.2 제약 전파: CSP의 추론 ·········································································· 242 6.3 CSP를 위한 역추적 검색 ······································································· 250 6.4 CSP를 위한 국소 검색 ··········································································· 257 6.5 문제의 구조 ······························································································ 259 요약 ···································································································· 265 참고문헌 및 역사적 참고사항 ··························································· 266 PART III 지식, 추론, 계획 수립 CHAPTER 7 논리적 에이전트 273 7.1 지식 기반 에이전트 ················································································· 274 7.2 웜퍼스 세계 ······························································································ 276 7.3 논리 ·········································································································· 280 7.4 명제 논리: 아주 간단한 논리 ································································· 284 7.5 명제 정리 증명 ························································································ 291 7.6 효과적인 명제 모형 점검 ········································································ 304 7.7 명제 논리에 기초한 에이전트 ································································· 310 요약 ···································································································· 322 참고문헌 및 역사적 참고사항 ··························································· 323 CHAPTER 8 1차 논리 327 8.1 표현의 재고찰 ·························································································· 327 8.2 1차 논리의 구문과 의미론 ······································································· 333 8.3 1차 논리의 활용 ······················································································· 346 8.4 1차 논리의 지식 공학 ·············································································· 354 요약 ···································································································· 361 참고문헌 및 역사적 참고사항 ··························································· 362 CHAPTER 9 1차 논리의 추론 365 9.1 명제 추론 대 1차 추론 ············································································ 365 9.2 단일화와 1차 추론 ··················································································· 368 9.3 순방향 연쇄 ······························································································ 374 9.4 역방향 연쇄 ······························································································ 382 9.5 분해 ·········································································································· 389 요약 ···································································································· 403 참고문헌 및 역사적 참고사항 ··························································· 404 CHAPTER 10 지식 표현 409 10.1 온톨로지 공학 ························································································ 410 10.2 범주와 객체 ···························································································· 413 10.3 사건 ········································································································ 420 10.4 정신적 객체와 양상 논리 ······································································ 425 10.5 범주 추론 시스템 ··················································································· 429 10.6 기본 정보를 이용한 추론 ······································································ 434 요약 ·································································································· 440 참고문헌 및 역사적 참고사항 ························································· 441 CHAPTER 11 자동 계획 수립 449 11.1 고전적 계획 수립의 정의 ······································································ 450 11.2 고전적 계획 수립을 위한 알고리즘들 ··················································· 455 11.3 계획 수립을 위한 발견적 함수 ····························································· 460 11.4 위계적 계획 수립 ··················································································· 465 11.5 비결정론적 정의역에서의 계획 수립과 실행 ········································ 476 11.6 시간, 일정, 자원 ···················································································· 488 11.7 계획 수립 접근방식들의 분석 ······························································· 493 요약 ·································································································· 494 참고문헌 및 역사적 참고사항 ························································· 495 PART IV 불확실한 지식과 추론 CHAPTER 12 불확실성의 정량화 505 12.1 불확실성하에서의 행동 ·········································································· 505 12.2 기본적인 확률 표기법 ············································································ 510 12.3 완전 결합 분포를 이용한 추론 ····························································· 518 12.4 독립성 ····································································································· 522 12.5 베이즈 규칙과 그 용법 ·········································································· 523 12.6 단순 베이즈 모형 ··················································································· 528 12.7 웜퍼스 세계의 재고찰 ············································································ 530 요약 ·································································································· 534 참고문헌 및 역사적 참고사항 ························································· 535 CHAPTER 13 확률적 추론 539 13.1 불확실한 문제 영역의 지식 표현 ·························································· 539 13.2 베이즈망의 의미론 ················································································· 542 13.3 베이즈망의 정확 추론 ············································································ 558 13.4 베이즈망의 근사 추론 ············································································ 568 13.5 인과망 ····································································································· 585 요약 ·································································································· 591 참고문헌 및 역사적 참고사항 ························································· 591 CHAPTER 14 시간에 따른 확률적 추론 599 14.1 시간과 불확실성 ····················································································· 600 14.2 시간적 모형의 추론 ··············································································· 605 14.3 은닉 마르코프 모형 ··············································································· 615 14.4 칼만 필터 ······························································································· 622 14.5 동적 베이즈망 ························································································ 630 요약 ·································································································· 643 참고문헌 및 역사적 참고사항 ························································· 644 CHAPTER 15 확률적 프로그래밍 647 15.1 관계 확률 모형 ······················································································ 648 15.2 열린 모집단 확률 모형 ·········································································· 656 15.3 복잡한 세계의 추적 ··············································································· 665 15.4 확률 모형으로서의 프로그램 ································································· 670 요약 ·································································································· 676 참고문헌 및 역사적 참고사항 ························································· 676 CHAPTER 16 간단한 의사결정 683 16.1 불확실성하에서의 믿음과 욕구의 결합 ················································· 684 16.2 효용이론의 기초 ····················································································· 685 16.3 효용 함수 ······························································································· 689 16.4 다중 특성 효용 함수 ············································································· 699 16.5 의사결정망 ······························································································ 705 16.6 정보의 가치 ···························································································· 708 16.7 미지의 선호도 ························································································ 716 요약 ·································································································· 720 참고문헌 및 역사적 참고사항 ························································· 721 CHAPTER 17 복잡한 의사결정 727 17.1 순차적 의사결정 문제 ············································································ 727 17.2 MDP를 위한 알고리즘들 ······································································· 740 17.3 강도 문제 ······························································································· 750 17.4 부분 관측 가능 MDP ············································································ 759 17.5 POMDP를 푸는 알고리즘 ····································································· 762 요약 ·································································································· 768 참고문헌 및 역사적 참고사항 ························································· 769 CHAPTER 18 다중 에이전트 의사결정 775 18.1 다중 에이전트 환경의 특징 ··································································· 775 18.2 비협력 게임 이론 ··················································································· 783 18.3 협력 게임 이론 ······················································································ 809 18.4 집합적 의사결정 ····················································································· 818 요약 ·································································································· 835 참고문헌 및 역사적 참고사항 ························································· 836 찾아보기 ·················································· 843 [2권] PART V 기계학습 CHAPTER 19 견본에서 배우는 학습 3 19.1 학습의 여러 형태 ······················································································· 4 19.2 지도학습 ····································································································· 6 19.3 결정 트리의 학습 ····················································································· 11 19.4 모형 선택과 최적화 ················································································· 21 19.5 학습 이론 ································································································· 30 19.6 선형 회귀와 분류 ····················································································· 35 19.7 비매개변수 모형 ······················································································· 47 19.8 앙상블 학습 ······························································································ 59 19.9 기계학습 시스템 개발 ·············································································· 69 요약 ···································································································· 81 참고문헌 및 역사적 참고사항 ··························································· 82 CHAPTER 20 확률 모형의 학습 89 20.1 통계적 학습 ······························································································ 90 20.2 완전 데이터를 이용한 학습 ····································································· 93 20.3 은닉 변수가 있는 학습: EM 알고리즘 ················································· 109 요약 ·································································································· 119 참고문헌 및 역사적 참고사항 ························································· 120 CHAPTER 21 심층학습 125 21.1 단순 순방향 신경망 ··············································································· 127 21.2 심층학습을 위한 계산 그래프 ······························································· 133 21.3 합성곱 신경망 ························································································ 137 21.4 학습 알고리즘 ························································································ 144 21.5 일반화 ····································································································· 148 21.6 순환 신경망 ···························································································· 153 21.7 비지도학습과 전이학습 ·········································································· 157 21.8 응용 ········································································································ 165 요약 ·································································································· 168 참고문헌 및 역사적 참고사항 ························································· 168 CHAPTER 22 강화학습 173 22.1 보상 기반 학습 ······················································································ 173 22.2 수동 강화학습 ························································································ 176 22.3 능동 강화학습 ························································································ 183 22.4 강화학습의 일반화 ················································································· 191 22.5 정책 검색 ······························································································· 199 22.6 견습 학습과 역강화학습 ········································································ 202 22.7 강화학습의 응용 ····················································································· 206 요약 ·································································································· 209 참고문헌 및 역사적 참고사항 ························································· 211 PART VI 의사소통, 지각, 행동 CHAPTER 23 자연어 처리 217 23.1 언어 모형 ······························································································· 218 23.2 문법 ········································································································ 231 23.3 파싱 ········································································································ 233 23.4 증강 문법 ······························································································· 240 23.5 실제 자연어의 복잡한 사항들 ······························································· 246 23.6 자연어 처리 과제들 ··············································································· 250 요약 ·································································································· 252 참고문헌 및 역사적 참고사항 ························································· 253 CHAPTER 24 자연어 처리를 위한 심층학습 259 24.1 단어 내장 ······························································································· 260 24.2 NLP를 위한 순환 신경망 ······································································ 264 24.3 순차열 대 순차열 모형 ·········································································· 268 24.4 트랜스포머 구조 ····················································································· 274 24.5 사전훈련과 전이학습 ·············································································· 277 24.6 현황 ········································································································ 282 요약 ·································································································· 285 참고문헌 및 역사적 참고사항 ························································· 285 CHAPTER 25 컴퓨터 시각 289 25.1 소개 ········································································································ 289 25.2 이미지 형성 ···························································································· 291 25.3 단순 이미지 특징 ··················································································· 298 25.4 이미지 분류 ···························································································· 306 25.5 물체 검출 ······························································································· 311 25.6 3차원 세계 ····························································································· 314 25.7 컴퓨터 시각의 용도 ··············································································· 319 요약 ·································································································· 334 참고문헌 및 역사적 참고사항 ························································· 335 CHAPTER 26 로봇공학 341 26.1 로봇 ······································································································ 341 26.2 로봇 하드웨어 ······················································································ 342 26.3 로봇공학이 푸는 문제들 ······································································ 347 26.4 로봇 지각 ····························································································· 349 26.5 계획 수립과 제어 ················································································· 357 26.6 불확실한 운동의 계획 ·········································································· 378 26.7 로봇공학의 강화학습 ············································································ 381 26.8 인간과 로봇 ·························································································· 384 26.9 로봇공학의 또 다른 틀 ········································································ 394 26.10 응용 영역 ····························································································· 397 요약 ································································································ 400 참고문헌 및 역사적 참고사항 ······················································· 402 PART VII 결론 CHAPTER 27 인공지능의 철학, 윤리학, 안전 411 27.1 인공지능의 한계 ····················································································· 411 27.2 기계가 정말로 생각할 수 있을까? ······················································· 416 27.3 인공지능의 윤리 ····················································································· 418 요약 ·································································································· 443 참고문헌 및 역사적 참고사항 ························································· 443 CHAPTER 28 인공지능의 미래 451 28.1 인공지능의 구성요소 ·············································································· 452 28.2 인공지능 구조 ························································································ 459 APPENDIX A 수학적 배경 465 A.1 복잡도 분석과 O( ) 표기법 ··································································· 465 A.2 벡터, 행렬, 선형대수 ············································································· 468 A.3 확률분포 ································································································· 470 참고문헌 및 역사적 참고사항 ························································· 473 APPENDIX B 언어와 알고리즘에 관해 475 B.1 BNF를 이용한 언어의 정의 ·································································· 475 B.2 알고리즘 서술에 쓰이는 의사코드 ························································ 476 B.3 온라인 보조 자료 ··················································································· 478 참고문헌 ·················································· 479 찾아보기 ·················································· 537 |
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저자 소개 (3명)
책 속으로 책속으로 보이기/감추기
앨런 튜링이 제안한(Turing, 1950) 튜링 검사(Turing test)는 “기계가 생각할 수 있는가?”라는 질문의 철학적 모호함을 피하는 하나의 사고 실험으로 고안되었다. 인간 조사자가 글로 쓴 질문에 대해 컴퓨터가 글로 답을 했을 때, 만일 그 답이 컴퓨터가 제출한 것인지 아니면 인간이 제출한 것인지 인간 조사자가 구분하지 못한다면 그 컴퓨터는 튜링 검사를 통과한 것이다.
--- p.4
필자는 어느 날 샹젤리제 거리를 걷다가 도로 건너편에서 옛 친구를 만난다. 오가는 차가 없고 특별히 할 일도 없어서, 합리적인 판단하에 도로를 건너기 시작한다. 그런데 3만 3천 피트 상공에서 화물 항공기의 화물실 문이 떨어져서, 필자는 결국 도로를 다 건너지 못하고 납작해진다... 도로를 건너기로 한 것이 비합리적이었을까? 필자의 부고에 “멍청하게도 도로를 건너려 했다.”라는 문구가 포함될 가능성은 작다.
이 예는 합리성이 완벽함과 같은 것이 아님을 보여 준다.
--- p.57
물론 그런 에이전트는 취약하다. 비천한 쇠똥구리를 생각해 보자. 땅에 구멍을 파서 알을 낳은 후 쇠똥구리는 근처의 배설물을 공 모양으로 만들어서 둥지의 구멍을 막는다.
그런데 둥지로 공을 굴리고 가는 도중에 공을 빼내면 쇠똥구리는 그 사실을 인식하지 못하고, 존재하지 않는 공을 굴려서 구멍을 막는 ‘팬터마임’을 한다. 진화에 의해 쇠똥구리의 행동에 하나의 가정이 삽입되었고, 그 가정이 위반되면 결과적으로 성공적이지 못한 행동이 빚어지는 것이다.
--- p.58
계획 감시 덕분에, 무려 480여 쪽 분량의 논의 후에 드디어 쇠똥구리(p.58)보다 더 똑똑한 에이전트가 등장했다! 계획 감시 에이전트는 자신이 쇠똥 공을 잡고 있지 않음을 감지하고, 다른 공을 구해서 구멍에 끼워 넣을 계획을 다시 수립할 것이다.
--- p.487
게임 이론은 게임 초기에는 다른 플레이어의 전략을 아직 알 수 없다는 개념을 나타내는 데 아주 적합하다. 그러나, 다른 플레이어들이 완전히 합리적이지는 않은 경우, 게임 이론은 다음에 할 일을 알려 주지 않는다. 베이즈-내시 균형(Bayes-Nash equilibrium)이라는 개념은 이 문제를 부분적으로 처리한다. 베이즈 내시 균형은 다른 플레이어들의 전략들에 대한 플레이어의 사전 확률분포를 기준으로 한 균형이다. 다른 말로 하면, 이 균형은 다른 플레이어들이 채용할 가능성이 있는 전략들에 대한 플레이어의 믿음들을 대표한다.
--- p.867
기계가 지능적으로 행동할 수 있다는 가능성 자체에 반대하면서 약 인공지능을 비판한 사람들이 있었는데, 지금 시점에서 보면 그런 사람들은 선견지명이 부족했다고 할 수 있다. 예를 들어 사이먼 뉴컴(Simon Newcomb)은 1903년 10월에 “공중 비행은 인간이 절대로 감당할 수 없는 어려운 문제 중 하나이다”라고 썼지만, 불과 두 달 후에 라이트 형제가 키티호크의 들판에서 유인 동력 비행에 성공했다. 그러나, 최근 인공지능이 급격히 발전했지만 그렇다고 인공지능의 능력에 한계가 없음이 증명된 것은 아니다.
--- p.412
이러한 신뢰성 문제가 두드러지게 노출된 사례가 있다. 1986년 9월 26일 소비에트 미사일 장교 스타니슬라프 페트로프의 컴퓨터 디스플레이에 미사일 공격 경보가 떴다.
프로토콜에 따라 페트로프는 핵 무기 반격 절차를 시작해야 했지만, 그는 그 경보가 시스템의 버그 때문이라고 의심하고는 조사해 보았다. 그가 옳았으며, 덕분에 인류는 제3차 세계대전을 (가까스로) 피할 수 있었다. 그 과정에서 인간의 개입이 없었다면 어떤 일이 일어났을지 우리는 알지 못한다.
--- p.422
효용 함수가 외부효과(externality)들 때문에 잘못될 수도 있다. 외부효과는 경제학에서 측정 및 지불 대상 바깥의 요인을 가리키는 용어이다. 온실 가스를 외부효과로 간주하면 기업과 국가는 온실 가스를 배출해도 벌칙을 받지 않으며, 결과적으로 지구의 모든 사람이 고통을 받는다. 생태학자 개릿 하딘은 공유 자원의 남용을 가리켜 공유지의 비극(tragedy of the commons)이라고 불렀다. 외부효과를 내부화하면, 즉 외부효과를 효용 함수의 일부로 두면 비극을 완화할 수 있다. 탄소세의 도입이 그런 내부화의 예이다.
--- p.439
굿의 ‘지능 폭발’을 수학 교수이자 과학소설 작가 버너 빈지(Vernor Vinge)는 기술적 특이점(technological singularity)이라고 불렀다. 1993년에 그는 “30년 내로 인류는 초인적 지능을 만들어 낼 기술적 수단들을 갖출 것이다. 얼마 후에는 인류의 시대가 끝날 것이다”라고 썼다(Vinge, 1993). 2017년에 발명가이자 미래학자인 레이 커즈와일(Ray Kurzweil)은 2045년이면 특이점이 나타날 것이라고 예측했다. 24년 만에 미래 시점이 30년에서 28년으로 2년 앞당겨진 것이다. (이 속도로 간다면 336년밖에 남지 않았다!) 기술적 진보의 여러 측정치가 현재 지수적으로 증가한다는 빈지와 커즈와일의 지적은 사실이다.
--- p.4
필자는 어느 날 샹젤리제 거리를 걷다가 도로 건너편에서 옛 친구를 만난다. 오가는 차가 없고 특별히 할 일도 없어서, 합리적인 판단하에 도로를 건너기 시작한다. 그런데 3만 3천 피트 상공에서 화물 항공기의 화물실 문이 떨어져서, 필자는 결국 도로를 다 건너지 못하고 납작해진다... 도로를 건너기로 한 것이 비합리적이었을까? 필자의 부고에 “멍청하게도 도로를 건너려 했다.”라는 문구가 포함될 가능성은 작다.
이 예는 합리성이 완벽함과 같은 것이 아님을 보여 준다.
--- p.57
물론 그런 에이전트는 취약하다. 비천한 쇠똥구리를 생각해 보자. 땅에 구멍을 파서 알을 낳은 후 쇠똥구리는 근처의 배설물을 공 모양으로 만들어서 둥지의 구멍을 막는다.
그런데 둥지로 공을 굴리고 가는 도중에 공을 빼내면 쇠똥구리는 그 사실을 인식하지 못하고, 존재하지 않는 공을 굴려서 구멍을 막는 ‘팬터마임’을 한다. 진화에 의해 쇠똥구리의 행동에 하나의 가정이 삽입되었고, 그 가정이 위반되면 결과적으로 성공적이지 못한 행동이 빚어지는 것이다.
--- p.58
계획 감시 덕분에, 무려 480여 쪽 분량의 논의 후에 드디어 쇠똥구리(p.58)보다 더 똑똑한 에이전트가 등장했다! 계획 감시 에이전트는 자신이 쇠똥 공을 잡고 있지 않음을 감지하고, 다른 공을 구해서 구멍에 끼워 넣을 계획을 다시 수립할 것이다.
--- p.487
게임 이론은 게임 초기에는 다른 플레이어의 전략을 아직 알 수 없다는 개념을 나타내는 데 아주 적합하다. 그러나, 다른 플레이어들이 완전히 합리적이지는 않은 경우, 게임 이론은 다음에 할 일을 알려 주지 않는다. 베이즈-내시 균형(Bayes-Nash equilibrium)이라는 개념은 이 문제를 부분적으로 처리한다. 베이즈 내시 균형은 다른 플레이어들의 전략들에 대한 플레이어의 사전 확률분포를 기준으로 한 균형이다. 다른 말로 하면, 이 균형은 다른 플레이어들이 채용할 가능성이 있는 전략들에 대한 플레이어의 믿음들을 대표한다.
--- p.867
기계가 지능적으로 행동할 수 있다는 가능성 자체에 반대하면서 약 인공지능을 비판한 사람들이 있었는데, 지금 시점에서 보면 그런 사람들은 선견지명이 부족했다고 할 수 있다. 예를 들어 사이먼 뉴컴(Simon Newcomb)은 1903년 10월에 “공중 비행은 인간이 절대로 감당할 수 없는 어려운 문제 중 하나이다”라고 썼지만, 불과 두 달 후에 라이트 형제가 키티호크의 들판에서 유인 동력 비행에 성공했다. 그러나, 최근 인공지능이 급격히 발전했지만 그렇다고 인공지능의 능력에 한계가 없음이 증명된 것은 아니다.
--- p.412
이러한 신뢰성 문제가 두드러지게 노출된 사례가 있다. 1986년 9월 26일 소비에트 미사일 장교 스타니슬라프 페트로프의 컴퓨터 디스플레이에 미사일 공격 경보가 떴다.
프로토콜에 따라 페트로프는 핵 무기 반격 절차를 시작해야 했지만, 그는 그 경보가 시스템의 버그 때문이라고 의심하고는 조사해 보았다. 그가 옳았으며, 덕분에 인류는 제3차 세계대전을 (가까스로) 피할 수 있었다. 그 과정에서 인간의 개입이 없었다면 어떤 일이 일어났을지 우리는 알지 못한다.
--- p.422
효용 함수가 외부효과(externality)들 때문에 잘못될 수도 있다. 외부효과는 경제학에서 측정 및 지불 대상 바깥의 요인을 가리키는 용어이다. 온실 가스를 외부효과로 간주하면 기업과 국가는 온실 가스를 배출해도 벌칙을 받지 않으며, 결과적으로 지구의 모든 사람이 고통을 받는다. 생태학자 개릿 하딘은 공유 자원의 남용을 가리켜 공유지의 비극(tragedy of the commons)이라고 불렀다. 외부효과를 내부화하면, 즉 외부효과를 효용 함수의 일부로 두면 비극을 완화할 수 있다. 탄소세의 도입이 그런 내부화의 예이다.
--- p.439
굿의 ‘지능 폭발’을 수학 교수이자 과학소설 작가 버너 빈지(Vernor Vinge)는 기술적 특이점(technological singularity)이라고 불렀다. 1993년에 그는 “30년 내로 인류는 초인적 지능을 만들어 낼 기술적 수단들을 갖출 것이다. 얼마 후에는 인류의 시대가 끝날 것이다”라고 썼다(Vinge, 1993). 2017년에 발명가이자 미래학자인 레이 커즈와일(Ray Kurzweil)은 2045년이면 특이점이 나타날 것이라고 예측했다. 24년 만에 미래 시점이 30년에서 28년으로 2년 앞당겨진 것이다. (이 속도로 간다면 336년밖에 남지 않았다!) 기술적 진보의 여러 측정치가 현재 지수적으로 증가한다는 빈지와 커즈와일의 지적은 사실이다.
--- p.441
출판사 리뷰 출판사 리뷰 보이기/감추기
2010년대 인공신경망의 부활과 심층학습의 눈부신 성과를 반영한 인공지능 연구의 결정판!
2016년에 나온 제3판 번역서(2009년에 출간된 원서 3판을 번역)는 최신 연구 반영에 한계가 있었으나, 이번 제4판 번역서는 최근 성과(자연어 이해, 로봇공학, 컴퓨터 시각에 심층학습이 끼친 영향, 강화학습을 로봇공학에 적용하는 방법, 기계학습, 인공지능 윤리 등)를 충실하게 반영한, 2020년에 출간된 원서를 옮긴 것이라 이 분야의 ‘좀 더 통합된 상’을 원하는 여러 독자의 갈증을 해소하는 데 큰 도움이 될 것입니다.
전체적으로, 책의 약 25%가 완전히 새로운 내용이고 나머지 75%도 이 분야의 좀 더 통합된 상을 제시하기 위해 크게 변경되었으며, 이번 판에서 인용한 문헌의 22%는 2010년 이후에 출판된 것들입니다.
제4판에서 새로운 점들
■ 사람이 손으로 짜는 지식 공학보다는 기계학습에 좀 더 무게를 실었다. 기계학습은 가용 데이터와 컴퓨팅 자원이 증가하고 새로운 알고리즘들이 등장한 덕분에 큰 성공을 거두고 있다.
■ 심층학습, 확률적 프로그래밍, 다중 에이전트 시스템을 각각 개별적인 장(챕터)으로 두어서 좀 더 자세히 다룬다.
■ 자연어 이해, 로봇공학, 컴퓨터 시각에 관한 장들을 심층학습이 끼친 영향을 반영해서 수정했다.
■ 로봇공학 장에 사람과 상호작용하는 로봇에 관한 내용과 강화학습을 로봇공학에 적용하는 방법에 관한 내용이 추가되었다.
■ 이전에는 인공지능의 목표를 사람이 구체적인 효용 정보(목적함수)를 제공한다는 가정하에서 기대 효용을 최대화하려는 시스템을 만드는 것이라고 정의했다. 그러나 이번 판에서는 목적함수가 고정되어 있으며 인공지능 시스템이 목적함수를 알고 있다고 가정하지 않는다. 대신, 시스템은 자신이 봉사하는 인간의 진짜 목적이 무엇인지 확실하게 알지 못할 수 있다고 가정한다. 시스템은 반드시 자신이 무엇을 최대화할 것인지를 배워야 하며, 목적에 관해 불확실성이 존재하더라도 적절히 작동해야 한다.
■ 인공지능이 사회에 미치는 영향을 좀 더 자세하게 다루었다. 여기에는 윤리, 공정성, 신뢰, 안정성에 관한 핵심적인 문제들을 고찰한다.
■ 각 장 끝의 연습문제들을 온라인 사이트로 옮겼다. 덕분에 강사들의 요구와 이 분야 및 인공지능 관련 소프트웨어 도구의 발전에 맞게 연습문제들을 계속 추가, 갱신, 개선할 수 있게 되었다.
2016년에 나온 제3판 번역서(2009년에 출간된 원서 3판을 번역)는 최신 연구 반영에 한계가 있었으나, 이번 제4판 번역서는 최근 성과(자연어 이해, 로봇공학, 컴퓨터 시각에 심층학습이 끼친 영향, 강화학습을 로봇공학에 적용하는 방법, 기계학습, 인공지능 윤리 등)를 충실하게 반영한, 2020년에 출간된 원서를 옮긴 것이라 이 분야의 ‘좀 더 통합된 상’을 원하는 여러 독자의 갈증을 해소하는 데 큰 도움이 될 것입니다.
전체적으로, 책의 약 25%가 완전히 새로운 내용이고 나머지 75%도 이 분야의 좀 더 통합된 상을 제시하기 위해 크게 변경되었으며, 이번 판에서 인용한 문헌의 22%는 2010년 이후에 출판된 것들입니다.
제4판에서 새로운 점들
■ 사람이 손으로 짜는 지식 공학보다는 기계학습에 좀 더 무게를 실었다. 기계학습은 가용 데이터와 컴퓨팅 자원이 증가하고 새로운 알고리즘들이 등장한 덕분에 큰 성공을 거두고 있다.
■ 심층학습, 확률적 프로그래밍, 다중 에이전트 시스템을 각각 개별적인 장(챕터)으로 두어서 좀 더 자세히 다룬다.
■ 자연어 이해, 로봇공학, 컴퓨터 시각에 관한 장들을 심층학습이 끼친 영향을 반영해서 수정했다.
■ 로봇공학 장에 사람과 상호작용하는 로봇에 관한 내용과 강화학습을 로봇공학에 적용하는 방법에 관한 내용이 추가되었다.
■ 이전에는 인공지능의 목표를 사람이 구체적인 효용 정보(목적함수)를 제공한다는 가정하에서 기대 효용을 최대화하려는 시스템을 만드는 것이라고 정의했다. 그러나 이번 판에서는 목적함수가 고정되어 있으며 인공지능 시스템이 목적함수를 알고 있다고 가정하지 않는다. 대신, 시스템은 자신이 봉사하는 인간의 진짜 목적이 무엇인지 확실하게 알지 못할 수 있다고 가정한다. 시스템은 반드시 자신이 무엇을 최대화할 것인지를 배워야 하며, 목적에 관해 불확실성이 존재하더라도 적절히 작동해야 한다.
■ 인공지능이 사회에 미치는 영향을 좀 더 자세하게 다루었다. 여기에는 윤리, 공정성, 신뢰, 안정성에 관한 핵심적인 문제들을 고찰한다.
■ 각 장 끝의 연습문제들을 온라인 사이트로 옮겼다. 덕분에 강사들의 요구와 이 분야 및 인공지능 관련 소프트웨어 도구의 발전에 맞게 연습문제들을 계속 추가, 갱신, 개선할 수 있게 되었다.
회원리뷰 (1건)
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세트 낱권에 등록된 리뷰 포함
세트 낱권에 등록된 리뷰 포함
류광씨의 번역은 최악이다. 최소한 글은 말이 되야 된다. 이 책에서 임의로 페이지를 펼치고 이게 한국말인지 아닌지 확이해봐라. 번역이 잘되었다는건 원문에 충실하다라는 말은 맞다. 그런데 한국어로 읽었을때 이게 한국말이야?라고 생각이 든다면 그 책은 잘못된 책이다. 이 번역서는 매우 잘못된 책이다. 강력하게 비추한다. 돈 아깝다. 난 다시는 류광이란 사람의 번역서를 사;
리뷰제목
류광씨의 번역은 최악이다. 최소한 글은 말이 되야 된다. 이 책에서 임의로 페이지를 펼치고 이게 한국말인지 아닌지 확이해봐라. 번역이 잘되었다는건 원문에 충실하다라는 말은 맞다. 그런데 한국어로 읽었을때 이게 한국말이야?라고 생각이 든다면 그 책은 잘못된 책이다. 이 번역서는 매우 잘못된 책이다. 강력하게 비추한다. 돈 아깝다. 난 다시는 류광이란 사람의 번역서를 사지 않을 것이다. 왜냐고? 내가 영문읽고 이해하는게 더 편하기 때문이다.
올해의 책 추천평 (1건) 올해의 책 추천평 이동
