쉐도잉 연습: Scientists still don't know the answer to this infamous question - Charles Wallace & Dan Kwartler - YouTube로 영어 말하기 배우기

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After waking up alone in a locked room, two documents are slipped under your door: a note in an alien language and a detailed instruction manual in your language.
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After waking up alone in a locked room, two documents are slipped under your door: a note in an alien language and a detailed instruction manual in your language.
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The manual explains that for each alien character in the note, you should write an indicated corresponding symbol.
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Following this chart, you write a response that you slip out the door.
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And for the next several days, this exchange continues.
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Outside the room, alien scientists are thrilled because they believe you’re conversing with them.
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But you still have no idea what these characters mean.
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This scenario isn’t just a bizarre misunderstanding— it’s a valuable thought experiment for understanding artificial intelligence.
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Philosopher John Searle developed the original version of this premise in 1980, as a response to some of the AI work being done at the time.
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But while modern AI models don't work like those outdated machines or the prisoner in Searle’s hypothetical, the question motivating his thought experiment is still relevant.
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To quote Searle, he wanted to interrogate whether an “appropriately programmed computer literally has cognitive states.” In other words, if a computer looks like it understands something, does that mean it actually understands the way a human does?
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Searle’s question falls into a long tradition of exploring whether or not AI could have a mind like ours.
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But answering these inquiries is incredibly difficult because, as philosophers and cognitive scientists will tell you, we still don’t know how our minds work.
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Even our fundamental definitions are slippery!
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Theorists generally agree that concepts like understanding, sentience, and consciousness are all different, but also that they’re related, and we don’t know how.
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Worse still, our usual scientific tools struggle to help us understand these experiences.
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Consider drinking a cup of coffee.
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Scientists can observe the physical process of ingesting the coffee, and we can measure the chemical impacts of caffeine on your body.
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These things are objective realities.
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But your collective sensation of smelling, sipping, evaluating, and experiencing a morning routine is more than the sum of its parts.
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This is consciousness— your subjective experience of being alive.
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And despite major leaps in psychology, cognitive science, and neurology, researchers still don’t know how various firing neurons bring about this experience.
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So if we can't define consciousness and understanding or identify what's uniquely human about them, how can we possibly test for these states in computers?
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Assessments like the Turing Test propose that if a human can't tell they're conversing with a computer, that computer could be seen as having some internal cognition.
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But this scenario is exactly what Searle was criticizing!
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This computer might just have the appearance of understanding.
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And just like cognitive scientists struggling to map consciousness onto brain activity, today’s AI researchers know how they trained their creations, but not how AIs reach their exact conclusions.
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There are some ways in which modern machine learning models are less mysterious than their predecessors.
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Approaches like neural networks and deep learning are designed to mimic known elements of human cognition.
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Like us, these models excel at pattern recognition— they learn by becoming familiar with information and forming connections across data sets.
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This kind of processing arguably approaches Searle’s definition of understanding— but it also reveals a bias in his original question.
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Since humans learn through pattern recognition and we believe ourselves to be conscious, we might also be predisposed to think other beings who learn the same way are somehow closer to consciousness as well.
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To combat this bias, some theorists have developed a different metric; specifically, that a fully conscious AI could draw connections beyond the information in its data set.
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For example, one lab’s “artificial consciousness test” probes AIs that have no data about consciousness for information they could only acquire from being conscious.
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This might involve asking an AI if it understands dreaming, or can report having had dreams itself.
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Can it understand a story about body swapping, where consciousnesses are shuffled between physical forms?
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It’s unclear when or if an AI will be able to understand us the way we understand each other.
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But whatever happens, it’s up to us already conscious creatures to chart the path forward.

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인기 동영상

왜 이 영상을 통해 말하기 연습을 해야 할까?

이 영상은 인공지능과 인간의 이해를 탐구하는 깊이 있는 질문을 다루고 있습니다. 유튜브 영어 공부를 통해 이 영상을 활용하면, 먼저 과학적이고 철학적인 주제에 대해 이야기하는 저자들의 말하기 실력을 개선할 수 있습니다. 말하기 연습을 통해 사고의 흐름을 제대로 이해하고 자신의 의견을 효과적으로 표현하는 능력을 키울 수 있습니다. 또한, shadow speech 기술을 적용하면 이 복잡한 개념을 더욱 쉽게 소화하게 되면서 발음을 자연스럽게 하기에도 도움이 됩니다.

문맥 속의 문법 및 표현

영상에서 사용된 주요 구조 몇 가지를 살펴보겠습니다:

  • “If a computer looks like it understands something, does that mean it actually understands the way a human does?” - 이 조건문 구조를 이용하여 상황에 따른 질문을 만들어 볼 수 있습니다. 이를 통해 복잡한 질문을 하는 능력을 기를 수 있습니다.
  • “This scenario is not just a bizarre misunderstanding.” - 부정어를 사용하여 특정 주장을 강조하는 방법을 배울 수 있습니다. 영어 쉐도잉을 통해 자주 쓰이는 이러한 표현을 반복적으로 연습해 보세요.
  • “Researchers still don’t know how various firing neurons bring about this experience.” - 현재완료 시제를 사용하여 과거와 현재의 연관성을 설명하는 방법을 익힐 수 있습니다.

일반적인 발음 함정

영상에서 특히 주의해야 할 발음이 있습니다:

  • “consciousness” - 이 단어는 많은 영어 학습자들이 발음하기 어려워하는 단어 중 하나입니다. 'con-scious-ness'의 음절을 명확하게 나누어 발음 연습을 해보세요.
  • “neurons” - 이 단어에서도 'ne-urons'의 발음을 정확히 하는 것이 중요합니다. 특히 /n/ 소리와 /r/ 소리를 부드럽게 연결하여 발음해 보세요.
  • “sensation” - 'sen-sation' 소리의 연결을 통해 리듬을 자연스럽게 개발하는 연습이 필요합니다.

이러한 발음 연습을 꾸준히 진행하면 shadowspeaks 및 shadow speak 기술을 통해 더욱 유창한 의사소통 능력을 갖출 수 있습니다.

쉐도잉이란? 영어 실력을 빠르게 키우는 과학적 방법

쉐도잉(Shadowing)은 원래 전문 통역사 훈련을 위해 개발된 언어 학습 기법으로, 다언어 학자인 Dr. Alexander Arguelles에 의해 대중화된 방법입니다. 핵심 원리는 간단하지만 매우 강력합니다: 원어민의 영어를 들으면서 1~2초의 짧은 지연으로 즉시 소리 내어 따라 말하는 것——마치 '그림자(shadow)'처럼 화자를 따라가는 것입니다. 문법 공부나 수동적인 청취와 달리, 쉐도잉은 뇌와 입 근육이 동시에 실시간으로 영어를 처리하고 재현하도록 훈련합니다. 연구에 따르면 이 방법은 발음 정확도, 억양, 리듬, 연음, 청취력, 말하기 유창성을 크게 향상시킵니다. IELTS 스피킹 준비와 자연스러운 영어 소통을 원하는 분들에게 특히 효과적입니다.

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