본문 바로가기
KERD
논문KCI 등재

‘인공 지능이 인간답다’라 함은? -심층 신경망 언어 모델을 중심으로-

‘Is artificial intelligence human-like?’ -With a focus on a deep neural network language model-

이재민|임선희|전수경|박명관|구건우|김유영|최릉운
동서사상연구소 (Research Institute for East-West Thought)|2022.06|철학∙사상∙문화 (PHILOSOPHY·THOUGHT·CULTURE)|제39호|pp. 256-280|DOI 10.33639/ptc.2022..39.012

1

조회

0

다운로드

0

인용

0

참고문헌

키워드

인공 지능language learningartificial intelligence언어 학습language processinglinguistic ability언어 처리언어 능력/지식심층 신경망 언어 모델태생적 지식deep neural(-network) language model
주제분류기타인문학

초록

사람의 신경망을 컴퓨터 공학 기술로 구현한 것이 소위 심층 신경망이다. 이를 적용한 언어 모델이 사람의 언어 학습을 재현함에 따라, 인공 지능 신경망 언어 모델이 언어 학습과 처리에 있어 사람에 상응하는 수준에 도달하고 있다고 보고되면서 ‘인공 지능(신경망 언어 모델)이 (언어 학습과 처리에 있어) 인간답다’라는 말이 회자된다. 본고에서는 신경망 언어 모델을 중심으로 ‘인공 지능이 인간답다’라는 명제를 재검토한다. 이에 따라, 신경망 언어 모델이 사람과 같은 언어 학습과 처리의 양상을 보인다고 할 때, 두 학습 및 처리 시스템이 상동한지 아니면 어떤 차이점이 있는지를 해명한다. 나아가서, 두 시스템의 비교를 바탕으로, 사람을 대상으로 언어 학습과 처리에 관한 전통적 이슈들에 관하여, 사람과 대비하여 신경망 언어 모델은 어떤 특성을 보이는지 논의한다.

Abstract

The statement 'artificial intelligence is human-like' has recently been a buzzword as an AI/neural-network language model is reported to reach a human level in language learning and processing, with the model implemented by applying the so-called deep neural network to language learning. In this paper, we evaluate the proposition of 'artificial intelligence is human', concentrating on the neural network language model. Accordingly, when neural network language models exhibit aspects of language learning and processing that humans do, we need to clarify whether the two human and AI learning & processing systems are homologous or what differences there are between them. Furthermore, based on the comparison of the two systems, we discuss what lights neural network language models shed on traditional issues raised for human language acquisition and processing, such as nature vs. nurture, human linguistic ability, etc.

참고문헌 (48)

  1. Adam, G., "Predictive power of word surprisal for reading times is a linear function of language model quality", Proceedings of the 8th Workshop on Cognitive Modeling and Computational Linguistics (2018)
  2. Adi, Y., "Fine-grained analysis of sentence embeddings using auxiliary prediction tasks", 5th International Conference on Learning Representations (2017)
  3. Banko, M., "Scaling to very very large corpora for natural language disambiguation", Proceedings of the 39th annual meeting of the Association for Computational Linguistics (2001)
  4. Baroni M, "On the proper role of linguistically-oriented deep net analysis in linguistic theorizing", arXiv:2106.08694, ~ (2021)
  5. Bock, K., "Broken agreement", Cogn. Psychol, 23 (1991)
  6. Chaves, R. P., "What Don't RNN Language Models Learn About Filler-Gap Dependencies?", Proceedings of the Society for Computation in Linguistics, 3(1) (2020)
  7. Cho, K., "Learning phrase representations using RNN EncoderDecoder for statistical machine translation", Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) (2014)
  8. Chomsky, N., "Handbook of Mathematical Psychology Vol.2" (1963)
  9. Chomsky, N, "Aspects of the Theory of Syntax" (1965), MIT Press
  10. Chomsky, N., "Rules and representations", Behav. Brain Sci, 3 (1980)
  11. Chomsky, N, "The Minimalist Program" (1995), MIT Press
  12. Christiansen, M., "Connectionist natural language processing:the state of the art", Cogn. Sci, 23 (1999)
  13. Chrupała, G., "Learning language through pictures", The 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, 2 (2015)
  14. Churchland, P, "A Neurocomputational Perspective: The Nature of Mind and the Structure of Science" (1989), MIT Press
  15. Clark, A, "Microcognition: Philosophy, Cognitive Science, and Parallel Distributed Processing" (1989), MIT Press
  16. Devlin, J., "BERT: pre-training of deep bidirectional transformers for language understanding", Proceedings of the 2016 Conference of the North American Chapter of association for Computational Linguistics: Human Language technologies, 1 (2016)
  17. Ettinger, A., "What BERT is not: Lessons from a new suite of psycholinguistic diagnostics for language models", Transactions of the Association for Computational Linguistics (2020)
  18. Fodor, J, "Connectionism and cognitive architecture: a critical analysis", Cognition, 28 (1988)
  19. Futrell, R., "Nerual language models as psycholinguistic subjects:Representation of syntatic state", Proceedings of the 2019 conference of the North American Chapter of the Association for Computational Linguistics: Human Language Techonologies, 1 (2019)
  20. Gibson, E, "Memory limitations and structural forgetting: the perception of complex ungrammatical sentences as grammatical", Lang. Cogn. Process, 14 (1999)
  21. Giulianelli, M., "Under the hood: Using diagnostic classifiers to investigate and improve how language models track agreement information", arXiv:1808.08079, ~ (2018)
  22. Gómez, R. L., "Infant artificial language learning and language acquisition", Trends in cognitive sciences (2000)
  23. Gulordava, K., "Colorless green recurrent networks dream hierarchically", Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics:Human Language Technologies, 1 (2018)
  24. Hart, B., "Meaningful differences in the everyday experience of young American children" (1995), Paul H Brookes Publishing
  25. Hauser, M., "The faculty of language: What is it, who has it, and how did it evolve?", Science, 298 (2002)
  26. Hewitt, J, "A structural probe for finding syntax in word representations", Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (2019)
  27. Hochreiter, S., "Long short-term memory", Neural Comput, 9 (1997)
  28. Hu, J., "A systematic assessment of syntactic generalization in neural language models", arXiv:2005.03692, ~ (2020)
  29. Khandelwal, U., "Sharp nearby, fuzzy far away: How neural language models use context", arXiv:1805.04623, ~ (2018)
  30. Lakoff, G., "The metaphorical structure of the human conceptual system", Cognitive science (1980)
  31. Lakretz, Y., "The emergence of number and syntax units in LSTM language models", Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (2019)
  32. Lasnik, H., "Oxford Handbook of Universal Grammar" (2017), Oxford Univ. Pres
  33. Linzen, T., "Syntactic structure from deep learning", Annual Review of Linguistics (2021)
  34. Linzen, T., "Distinct patterns of syntactic agreement errors in recurrent networks and humans", Proceedings of the 40th Annual Conference of the Cognitive Science Society (2018)
  35. Linzen, T., "Assessing the ability of LSTMs to learn syntax-sensitive dependencies", Trans. Assoc. Comput. Linguist, 4 (2016)
  36. Marvin, R., "Targeted Syntactic Evaluation of Language Models", Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (2018)
  37. The need for biases in learning generalizations, "Mitchell, T. M.", Rutgers Univ, ~ (1980)
  38. Pinker, S, "On language and connectionism: analysis of a parallel distributed processing model of language acquisition", Cognition, 28 (1988)
  39. Radford, A., "Language models are unsupervised multitask learners", OpenAI blog, 9 (2019)
  40. Raffel, C., "Exploring the limits of transfer learning with a unified text-to-text transforme", arXiv:1910.10683, ~ (2019)
  41. Shi, X., "Does string-based neural MT learn source syntax?", Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing (2016)
  42. van Schijndel, M., "Single‐stage prediction models do not explain the magnitude of syntactic disambiguation difficulty", Cognitive Science, 45(6) (2021)
  43. van Schijndel, M., "Modeling garden path effects without explicit hierarchical syntax", Cognition (2018)
  44. Warstadt, A., "BLiMP: The Benchmark of Linguistic Minimal Pairs for English", Transactions of the Association for Computational Linguistics (2020)
  45. Warstadt, A., "Neural Network Acceptability Judgments", Transactions of the Association for Computational Linguistics (2019)
  46. Weston, J. E., "Dialog-based language learning", Advances in Neural Information Processing Systems (2016)
  47. Wilcox, E. G., "On the predictive power of neural language models for human real-time comprehension behavior", arXiv:2006.01912, ~ (2020)
  48. Wilcox, E., "A targeted Assessment of Incremental Processing in Neural Language Models and Humans", Computation and Language (2021)

관련 논문AI추천

인공 지능은 상식을 배울 수 있을까?박명관동서사상연구소 · 철학∙사상∙문화2023 · 제41호뇌는 인공지능처럼 언어를 학습하는가?: 고빈도 입력데이터와 언어학습이혜문외국어교육연구소 · 외국어교육연구2023 · 제37권 제1호신경망 언어 모델의 내부 표상: 탐침 분류기 기법을 중심으로구건우, 김유영, 박명관, 이재민, 임선희, 전수경, 최릉운대한영어영문학회 · 영어영문학연구2022 · 제48권 제3호딥러닝 언어모형의 평가와 언어학송상헌언어정보연구소 · 언어와 정보 사회2022 · 제45권 빈도 효과에 기반한 DBN 알고리즘의 언어 획득 모델링 적용 가능성 탐구구민모, 남기춘, 유희조, 이광오한국인지및생물심리학회 · 한국심리학회지: 인지 및 생물2016 · 제28권 제4호인공지능의 의식에 관한 신경과학적 고찰:‘람다’와 ‘클로드 오퍼스4’ 사례를 중심으로조정호한국인격교육학회 · 인격교육2025 · 제19권 제4호

이 간행물의 다른 논문 (15건)

신경 향상 기술 관련 신경윤리 연구를 위한 시나리오 개발 -뇌심부자극술(DBS)을 통한 기억 향상 기술을 중심으로-엄주희, 류영준pp. 1-17벤담의 의무론과 고통의 윤리적 알고리즘을 활용한 AI 윤리 수업 설계의 기초 -국가 교육과정의 개정과 내용 체계를 중심으로-송선영pp. 18-38진리 대응설에 대한 쿤과 퍼트넘의 대안이상원pp. 39-62미셸 앙리의 삶의 현상학 -후설의 지향성을 넘어 비지향적 파토스로-이은정pp. 63-84칸트철학의 오류론에서 계몽철학으로의 전개에 관한 연구이진원pp. 85-102
원문 보기