A Path Towards Autonomous Machine Intelligence
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125.240.91.97 (토론)님의 2022년 9월 12일 (월) 01:41 판 (새 문서: 인공지능 관련 잡지 기사를 읽다가 알게 된 얀 르쿤의 최신 논문. 아래는 Abstract 이고, 전체 PDF는 [https://openreview.net/pdf?id=BZ5a1r-kVsf 여기]에 있다. ([https://openreview.net/forum?id=BZ5a1r-kVsf 링크]) How could machines learn as efficiently as humans and animals? How could machines learn to reason and plan? How could machines learn representations of percepts and action plans at multiple levels of abstraction, enabling them to rea...)
125.240.91.97 (토론)님의 2022년 9월 12일 (월) 01:41 판 (새 문서: 인공지능 관련 잡지 기사를 읽다가 알게 된 얀 르쿤의 최신 논문. 아래는 Abstract 이고, 전체 PDF는 [https://openreview.net/pdf?id=BZ5a1r-kVsf 여기]에 있다. ([https://openreview.net/forum?id=BZ5a1r-kVsf 링크]) How could machines learn as efficiently as humans and animals? How could machines learn to reason and plan? How could machines learn representations of percepts and action plans at multiple levels of abstraction, enabling them to rea...)
인공지능 관련 잡지 기사를 읽다가 알게 된 얀 르쿤의 최신 논문. 아래는 Abstract 이고, 전체 PDF는 여기에 있다. (링크)
How could machines learn as efficiently as humans and animals? How could machines learn to reason and plan? How could machines learn representations of percepts and action plans at multiple levels of abstraction, enabling them to reason, predict, and plan at multiple time horizons? This position paper proposes an architecture and training paradigms with which to construct autonomous intelligent agents. It combines concepts such as configurable predictive world model, behavior driven through intrinsic motivation, and hierarchical joint embedding architectures trained with self-supervised learning.
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