August 2021
Beginner to intermediate
720 pages
15h 32m
Korean
Content preview from fastai와 파이토치가 만나 꽃피운 딥러닝
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167
3
데이터 윤리
자동 생성한 텍스트로 전달하는 허위 정보는 딥러닝의 가능성이 크게 향상된 최근 특히 중요한
문제입니다. 언어 모델을 만드는
10
장에서 이를 자세히 다룹니다.
한 가지 접근법은 일종의 전자 서명을 개발하고 손쉽게 확장할 수 있도록 구현하여 검증된 콘
텐츠만 신뢰하도록 하는 규범을 만드는 것입니다. 앨런 인공지능 연구소
Allen
Institute
on
AI
의 책
임자인 오렌 에지오니
Oren
Etzioni
는 「
How
Will
We
Prevent
AI
-
Based
Forgery
?(
https
://
oreil
.
ly
/
8z7wm
)」 기사에서 다음과 같이 제안합니다. “
AI
는 저렴하고 자동화된 방식으로 매
우 정확한 위조를 가능케 합니다. 이는 잠재적으로 민주주의, 보안, 사회에 재앙을 초래할 수
있습니다. 실체가 없는
AI
위조는 디지털 콘텐츠의 인증 수단으로 디지털 서명을 만들어야 함
을 의미합니다.”
딥러닝과 알고리즘의 사용에서 일반적으로 제기되는 모든 윤리적 문제를 논의할 수는 없지만,
여기서 다룬 간략한 소개가 유용한 출발점이 되었기를 바랍니다. 이번에는 윤리적 문제를 식별
하고 대처하는 방법을 살펴보죠.
3.4
실수는 늘 발생합니다. 이를 발견하고 처리하는 일은 머신러닝을 포함해 모든 시스템 설계의
일부가 되어야만 합니다. 데이터 윤리에서 제기되는 문제에는 대개 여러 학문 분야가 관련되어
복잡합니다. 하지만 이를 해결하려고 노력해야만 합니다.
그래서 우리가 무엇을 할 수 있을까요? 이는 거대한 ...
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and much more.
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I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.