人工智能与基础相互作用研究所(IAIFI):将物理智能注入人工智能

IF 2.5 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Ai Magazine Pub Date : 2024-02-17 DOI:10.1002/aaai.12150
Jesse Thaler, Mike Williams, Marisa LaFleur
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引用次数: 0

摘要

国家自然科学基金人工智能与基础相互作用研究所(IAIFI,读作/aI-faI/)是首批国家自然科学基金人工智能研究所(https://iaifi.org)之一。IAIFI 通过开发融合基础物理学第一原理的新型人工智能方法,促进物理学的发现并推动基础人工智能的发展。通过将最先进的研究与波士顿地区及其他地区的早期职业人才和不断壮大的人工智能+物理学社区相结合,IAIFI 使研究人员能够开发人工智能技术,以解决物理学中一些最具挑战性的问题,并将这些技术转移到更广泛的人工智能社区。由于值得信赖的人工智能对于物理学发现和人工智能在社会中的其他应用同样重要,IAIFI 的研究人员正在应用物理学原理开发更强大的人工智能工具,并阐明现有的人工智能技术。为了培养人类智能,IAIFI 在物理学和人工智能的交叉领域促进培训、教育和公众参与。通过这些方式,IAIFI 正在将深度学习与深度思考相融合,以加深对我们的宇宙和人工智能的理解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Institute for Artificial Intelligence and Fundamental Interactions (IAIFI): Infusing physics intelligence into artificial intelligence

The NSF AI Institute for Artificial Intelligence and Fundamental Interactions (IAIFI, pronounced /aI-faI/) is one of the inaugural NSF AI research institutes (https://iaifi.org). The IAIFI is enabling physics discoveries and advancing foundational AI through the development of novel AI approaches that incorporate first principles from fundamental physics. By combining state-of-the-art research with early career talent and a growing AI + physics community in the Boston area and beyond, the IAIFI is enabling researchers to develop AI technologies to tackle some of the most challenging problems in physics, and transfer these technologies to the broader AI community. Since trustworthy AI is as important for physics discovery as it is for other applications of AI in society, IAIFI researchers are applying physics principles to develop more robust AI tools and to illuminate existing AI technologies. To cultivate human intelligence, the IAIFI promotes training, education, and public engagement at the intersection of physics and AI. In these ways, the IAIFI is fusing deep learning with deep thinking to gain a deeper understanding of our universe and AI.

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来源期刊
Ai Magazine
Ai Magazine 工程技术-计算机:人工智能
CiteScore
3.90
自引率
11.10%
发文量
61
审稿时长
>12 weeks
期刊介绍: AI Magazine publishes original articles that are reasonably self-contained and aimed at a broad spectrum of the AI community. Technical content should be kept to a minimum. In general, the magazine does not publish articles that have been published elsewhere in whole or in part. The magazine welcomes the contribution of articles on the theory and practice of AI as well as general survey articles, tutorial articles on timely topics, conference or symposia or workshop reports, and timely columns on topics of interest to AI scientists.
期刊最新文献
Issue Information AI fairness in practice: Paradigm, challenges, and prospects Toward the confident deployment of real-world reinforcement learning agents Towards robust visual understanding: A paradigm shift in computer vision from recognition to reasoning Efficient and robust sequential decision making algorithms
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