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    Tokenization: The Identity Crisis of the Word and AI History

    20분
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    2026년 5월 2일
    AITechnologyScience

    Explore the history of AI from Joseph Weizenbaum's ELIZA to modern tokenization. Learn how computers process words through pattern matching and NLP history.

    Tokenization: The Identity Crisis of the Word and AI History

    Tokenization: The Identity Crisis of the Word and AI History 베스트 인용

    “

    Language, for a machine, is never 'natural.' Every word you type is being sliced into tokens, mapped to Unicode code points, and measured against a massive statistical history of other words.

    ”

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    질문 입력

    https://web.stanford.edu/~jurafsky/slp3/2.pdf

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    https://web.stanford.edu/~jurafsky/slp3/2.pdf
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    https://web.stanford.edu/~jurafsky/slp3/2.pdf

    자주 묻는 질문

    Tokenization is the essential process where a computer determines what a word is before it can process language. This invisible engine powers every AI interaction today, solving the fundamental challenge of how machines identify and interpret human speech. It represents the bridge between raw text and machine understanding, a concept that has evolved significantly since the early days of computer science and natural language processing experiments at institutions like MIT.

    Joseph Weizenbaum was a researcher at an MIT computer lab who created ELIZA in 1966. ELIZA was a groundbreaking program designed to mimic a Rogerian psychotherapist, marking the first time a machine truly talked back to humans. Although it used simple pattern matching to transform user input into questions, it had a profound psychological impact on users who believed the machine genuinely understood their personal problems and family history.

    ELIZA functioned by using basic pattern matching on words to recognize specific phrases and transform them into responses. For example, if a user typed a phrase starting with 'I need,' the program was designed to respond with 'What would it mean to you if you got...' followed by the user's request. This crude mimicry created an illusion of depth, making users feel as though the machine was peering into their souls despite its simple underlying logic.

    The ELIZA experiment remains relevant because it highlights the core challenge modern smartphones and AI still face: the identity crisis of the word. Before any advanced AI can understand a user, it must first undergo tokenization to define language boundaries. ELIZA was the first moment researchers confronted the psychological impact of chatbots, a phenomenon that continues to influence how we develop and interact with natural language processing technologies today.

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