NOW BUZZING 어그가 로퍼를 만든 이유, JW 앤더슨과… · 유니클로가 '다크 아카데미아'를 입기 쉽… · 리미 푸가 스케이트보드를 옷처럼 파는 법◆
NOW BUZZING 어그가 로퍼를 만든 이유, JW 앤더슨과… · 유니클로가 '다크 아카데미아'를 입기 쉽… · 리미 푸가 스케이트보드를 옷처럼 파는 법◆
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GADGET BEHIND IT · 5 MIN READ

신경망이 컴퓨터를 인간처럼 학습하게 하는 원리

JD 젤리비 편집국 · 2026.01.07 SHARE · COPY LINK

무슨 발표인가

  • 신경망으로 컴퓨터가 고양이·개를 인간 수준으로 구별 가능
  • 컴퓨터 비전·머신러닝 발전의 핵심 기술
  • Google Health 팀이 의료 영상 해석에 적용 연구

원문 (영어)

Back in the day, there was a surefire way to tell humans and computers apart: You’d present a picture of a four-legged friend and ask if it was a cat or dog. A computer couldn’t identify felines from canines, but we humans could answer with doggone confidence.

That all changed about a decade ago thanks to leaps in computer vision and machine learning – specifically, major advancements in neural networks, which can train computers to learn in a way similar to humans. Today, if you give a computer enough images of cats and dogs and label which is which, it can learn to tell them apart purr-fectly.

But how exactly do neural networks help computers do this? And what else can — or can’t — they do? To answer these questions and more, I sat down with Google Research’s Maithra Raghu , a research scientist who spends her days helping computer scientists better understand neural networks.

Her research helped the Google Health team discover new ways to apply deep learning to assist doctors and their patients. So, the big question: What’s a neural network? To understand neural networks, we need to first go back to the basics and understand how they fit into the bigger picture of artificial intelligence (AI).

Imagine a Russian nesting doll, Maithra explains. AI would be the largest doll, then within that, there’s machine learning (ML), and within that, neural networks (... and within that, deep neural networks, but we’ll get there soon!). If you think of AI as the science of making things smart, ML is the subfield of AI focused on making computers smarter by teaching them to learn, instead of hard-coding them.

Within that, neural networks are an advanced technique for ML, where you teach computers to learn with algorithms that take inspiration from the human brain.

원문: Google Blog (Technology/AI) — "Ask a Techspert: What’s a neural network?" (2026-01-07) 공식 원문: https://blog.google/innovation-and-ai/technology/ai/ask-a-techspert-whats-a-neural-network/

#Google Blog (Technology/AI)
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신경망이 컴퓨터를 인간처럼 학습하게 하는 원리

젤리비 편집국·2026.01.07·5 MIN
IN THIS PIECE
무슨 발표인가 원문 (영어)

무슨 발표인가

  • 신경망으로 컴퓨터가 고양이·개를 인간 수준으로 구별 가능
  • 컴퓨터 비전·머신러닝 발전의 핵심 기술
  • Google Health 팀이 의료 영상 해석에 적용 연구

원문 (영어)

Back in the day, there was a surefire way to tell humans and computers apart: You’d present a picture of a four-legged friend and ask if it was a cat or dog. A computer couldn’t identify felines from canines, but we humans could answer with doggone confidence.

That all changed about a decade ago thanks to leaps in computer vision and machine learning – specifically, major advancements in neural networks, which can train computers to learn in a way similar to humans. Today, if you give a computer enough images of cats and dogs and label which is which, it can learn to tell them apart purr-fectly.

But how exactly do neural networks help computers do this? And what else can — or can’t — they do? To answer these questions and more, I sat down with Google Research’s Maithra Raghu , a research scientist who spends her days helping computer scientists better understand neural networks.

Her research helped the Google Health team discover new ways to apply deep learning to assist doctors and their patients. So, the big question: What’s a neural network? To understand neural networks, we need to first go back to the basics and understand how they fit into the bigger picture of artificial intelligence (AI).

Imagine a Russian nesting doll, Maithra explains. AI would be the largest doll, then within that, there’s machine learning (ML), and within that, neural networks (... and within that, deep neural networks, but we’ll get there soon!). If you think of AI as the science of making things smart, ML is the subfield of AI focused on making computers smarter by teaching them to learn, instead of hard-coding them.

Within that, neural networks are an advanced technique for ML, where you teach computers to learn with algorithms that take inspiration from the human brain.

원문: Google Blog (Technology/AI) — "Ask a Techspert: What’s a neural network?" (2026-01-07) 공식 원문: https://blog.google/innovation-and-ai/technology/ai/ask-a-techspert-whats-a-neural-network/

#Google Blog (Technology/AI)
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