Physics Inspired Models in Artificial Intelligence

被引:1
作者
Ahmad, Muhammad Aurangzeb [1 ]
Ozonder, Sener [2 ]
机构
[1] Univ Washington Tacoma, Dept Comp Sci, Seattle, WA 98402 USA
[2] Istinye Univ, Elect & Elect Engn Dept, Istanbul, Turkey
来源
KDD '20: PROCEEDINGS OF THE 26TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING | 2020年
关键词
artificial intelligence; physics; ai and physics; physics inspired models; machine learning and physics;
D O I
10.1145/3394486.3406464
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Ideas originating in physics have informed progress in artificial intelligence and machine learning for many decades. However the pedigree of many such ideas is oft neglected in the Computer Science community. The tutorial focuses on current and past ideas from physics that have helped in furthering AI and machine learning. Recent advances in physics inspired ideas in AI are also explored especially how insights from physics may hold the promise of opening the black box of deep learning. Lastly, current and future trends in this area and outlines of a research agenda on how physics-inspired models can benefit AI machine learning is given.
引用
收藏
页码:3535 / 3536
页数:2
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