AI Institute: AI Research Institute for Fundamental Interactions
AI Institute: AI Research Institute for Fundamental Interactions
批准号:
2019786
负责人:
Jesse Thaler
金额:
$2000.0万
依托单位国家:
美国
项目类别:
Cooperative Agreement
财政年份:
2020
资助国家:
美国
项目状态:
未结题
起止时间:
2020-11-01 至 2026-04-30
中文摘要
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英文摘要
The Institute for Artificial Intelligence and Fundamental Interactions (IAIFI) will enable physics discoveries and advance foundational artificial intelligence (AI) through the development of novel AI approaches that incorporate first principles from fundamental physics. AI is transforming many aspects of society, including the ways that scientists are pursuing groundbreaking discoveries. For many years, physicists have been at the forefront of applying AI methods to investigate fundamental questions about the Universe. As an example, AI played a key role in the discovery and study of the Higgs boson, the last missing ingredient in the Standard Model of particle physics. Further progress will require a revolutionary leap in AI, as both the complexity of physics problems and the size of physics datasets continue to grow. The goal of the IAIFI is to develop and deploy the next generation of AI technologies, based on the transformative idea that artificial intelligence can directly incorporate physics intelligence. IAIFI researchers will use these new AI technologies to tackle some of the most challenging problems in physics, from precision calculations of the structure of matter, to gravitational wave detection of merging black holes, to the extraction of new physical laws from noisy data. IAIFI researchers will also 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. To cultivate human intelligence, the IAIFI will promote training, education, and outreach at the intersection of physics and AI. In this way, the IAIFI will advance physics knowledge – from the smallest building blocks of nature to the largest structures in the Universe – and galvanize AI research innovation.The IAIFI will enable physics discoveries and advance foundational AI through the development of novel “Ab initio AI” approaches that incorporate first principles and best practices from fundamental physics. Ab initio AI will make intractable theoretical physics calculations feasible, predicting complex emergent phenomena that are computationally daunting to tackle even though the underlying physical laws are well understood. It will also transform many experimental physics applications, where ab initio principles will be used to design AI methods that are more easily verifiable using well-understood calibration data samples, leading to better quantification of uncertainties. While each physics use-inspired goal will present its own issues, the IAIFI’s focus will be on finding shared solutions, since these problems involve similar prior knowledge, are based on the same underlying ab initio principles, and face common experimental and theoretical challenges. The same challenges that arise in the development and deployment of AI methods across a broad spectrum of frontier physics research, including verification and interpretability of AI solutions, are also faced in other AI application domains. Therefore, by developing ab initio AI, the IAIFI will accelerate the pace of physics discovery, extend the frontiers of AI research, and develop new pathways for broad adoption. The IAIFI will make Cambridge and the surrounding Boston area a nexus point for collaborative efforts aimed at advancing both physics and AI and at connecting to industry and science partnerships. Part of the IAIFI mission will be to disseminate knowledge about (and enthusiasm for) physics, AI, and their intersection through various workforce development, digital learning, outreach, broadening participation, and knowledge transfer programs.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
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DOI:
10.1103/physrevd.106.103509
发表时间:
2022-04
期刊:
Physical Review D
影响因子:
5
作者:
[Georgios Valogiannis;C. Dvorkin]
通讯作者:
Georgios Valogiannis;C. Dvorkin
Single electrons on solid neon as a solid-state qubit platform
固体氖上的单电子作为固态量子位平台
DOI:
10.1038/s41586-022-04539-x
发表时间:
2022
期刊:
Nature
影响因子:
64.8
作者:
[Zhou, Xianjing, Koolstra, Gerwin, Zhang, Xufeng, Yang, Ge, Han, Xu, Dizdar, Brennan, Li, Xinhao, Divan, Ralu, Guo, Wei, Murch, Kater W.]
通讯作者:
Murch, Kater W.
DOI:
10.1109/cvpr52688.2022.00541
发表时间:
2021-12
期刊:
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
影响因子:
--
作者:
[Dor Verbin;Peter Hedman;B. Mildenhall;Todd E. Zickler;J. Barron;Pratul P. Srinivasan]
通讯作者:
Dor Verbin;Peter Hedman;B. Mildenhall;Todd E. Zickler;J. Barron;Pratul P. Srinivasan
DOI:
10.1021/acs.nanolett.2c03307
发表时间:
2022-02
期刊:
Nano letters
影响因子:
10.8
作者:
[Andrew Ma;Yang Zhang;Thomas Christensen;H. Po;Li Jing;L. Fu;M. Soljavci'c]
通讯作者:
Andrew Ma;Yang Zhang;Thomas Christensen;H. Po;Li Jing;L. Fu;M. Soljavci'c
DOI:
10.1103/physrevd.104.114507
发表时间:
2021-06
期刊:
ArXiv
影响因子:
--
作者:
[M. S. Albergo;G. Kanwar;S. Racanière;Danilo Jimenez Rezende;Julian M. Urban;D. Boyda;Kyle Cranmer;D. Hackett;P. Shanahan]
通讯作者:
M. S. Albergo;G. Kanwar;S. Racanière;Danilo Jimenez Rezende;Julian M. Urban;D. Boyda;Kyle Cranmer;D. Hackett;P. Shanahan
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