Ai Magazine最新文献_第9页

AI Institute in Dynamic Systems: Developing machine learning and AI tools for scientific discovery, engineering design, and data-driven control 动态系统人工智能研究所：为科学发现、工程设计和数据驱动控制开发机器学习和人工智能工具

IF 0.9 4区计算机科学

Ai Magazine Pub Date : 2024-02-21 DOI: 10.1002/aaai.12159

J. Nathan Kutz, Steven L. Brunton, Krithika Manohar, Hod Lipson, Na Li

{"title":"AI Institute in Dynamic Systems: Developing machine learning and AI tools for scientific discovery, engineering design, and data-driven control","authors":"J. Nathan Kutz, Steven L. Brunton, Krithika Manohar, Hod Lipson, Na Li","doi":"10.1002/aaai.12159","DOIUrl":"https://doi.org/10.1002/aaai.12159","url":null,"abstract":"The mission of the AI Institute in Dynamic Systems is to develop the next generation of advanced machine learning (ML) and AI tools for controlling complex physical systems by discovering physically interpretable and physics-constrained data-driven models through optimal sensor selection and placement. The research effort is anchored by a common task framework (CTF) that evaluates the performance of ML algorithms, architectures, and optimization schemes for the diverse tasks required in engineering applications. The aim is to push beyond the boundaries of modern techniques by closing the loop between data collection, control, and modeling, creating a unique and cross-disciplinary architecture for learning physically interpretable and physics constrained models of complex dynamic systems from time series data. The CTF further supports sustainable and open-source challenge datasets, which are foundational for developing interpretable, ethical, and inclusive tools to solve problems fundamental to human safety, society, and the environment.","PeriodicalId":7854,"journal":{"name":"Ai Magazine","volume":"45 1","pages":"48-53"},"PeriodicalIF":0.9,"publicationDate":"2024-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/aaai.12159","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140164370","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The TILOS AI Institute: Integrating optimization and AI for chip design, networks, and robotics TILOS 人工智能研究所：将优化和人工智能整合到芯片设计、网络和机器人技术中

IF 0.9 4区计算机科学

Ai Magazine Pub Date : 2024-02-20 DOI: 10.1002/aaai.12165

Andrew B. Kahng, Arya Mazumdar, Jodi Reeves, Yusu Wang

引用次数: 0

AI-ALOE: AI for reskilling, upskilling, and workforce development AI-ALOE：人工智能促进技能再培训、技能提升和劳动力发展

IF 0.9 4区计算机科学

Ai Magazine Pub Date : 2024-02-19 DOI: 10.1002/aaai.12157

Ashok Goel, Chris Dede, Myk Garn, Chaohua Ou

引用次数: 0

Molecule Maker Lab Institute: Accelerating, advancing, and democratizing molecular innovation 分子制造实验室研究所：加速、推进和民主化分子创新

IF 0.9 4区计算机科学

Ai Magazine Pub Date : 2024-02-17 DOI: 10.1002/aaai.12154

Martin D. Burke, Scott E. Denmark, Ying Diao, Jiawei Han, Rachel Switzky, Huimin Zhao

{"title":"Molecule Maker Lab Institute: Accelerating, advancing, and democratizing molecular innovation","authors":"Martin D. Burke, Scott E. Denmark, Ying Diao, Jiawei Han, Rachel Switzky, Huimin Zhao","doi":"10.1002/aaai.12154","DOIUrl":"10.1002/aaai.12154","url":null,"abstract":"Many of the greatest challenges facing society today likely have molecular solutions that await discovery. However, the process of identifying and manufacturing such molecules has remained slow and highly specialist dependent. Interfacing the fields of artificial intelligence (AI) and synthetic organic chemistry has the potential to powerfully address both limitations. The Molecule Maker Lab Institute (MMLI) brings together a team of chemists, engineers, and AI-experts from the University of Illinois Urbana-Champaign (UIUC), Pennsylvania State University, and the Rochester Institute of Technology, with the goal of accelerating the discovery, synthesis and manufacture of complex organic molecules. Advanced AI and machine learning (ML) methods are deployed in four key thrusts: (1) AI-enabled synthesis planning, (2) AI-enabled catalyst development, (3) AI-enabled molecule manufacturing, and (4) AI-enabled molecule discovery. The MMLI's new AI-enabled synthesis platform integrates chemical and enzymatic catalysis with literature mining and ML to predict the best way to make new molecules with desirable biological and material properties. The MMLI is transforming chemical synthesis and generating use-inspired AI advances. Simultaneously, the MMLI is also acting as a training ground for the next generation of scientists with combined expertise in chemistry and AI. Outreach efforts aimed toward high school students and the public are being used to show how AI-enabled tools can help to make chemical synthesis accessible to nonexperts.","PeriodicalId":7854,"journal":{"name":"Ai Magazine","volume":"45 1","pages":"117-123"},"PeriodicalIF":0.9,"publicationDate":"2024-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/aaai.12154","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139960233","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Generative AI: An AI paradigm shift in the making? 生成式人工智能：正在发生的人工智能范式转变？

IF 0.9 4区计算机科学

Ai Magazine Pub Date : 2024-02-17 DOI: 10.1002/aaai.12155

Risto Miikkulainen

引用次数: 0

Institute for Artificial Intelligence and Fundamental Interactions (IAIFI): Infusing physics intelligence into artificial intelligence 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 0.9 4区计算机科学

Ai Magazine Pub Date : 2024-02-17 DOI: 10.1002/aaai.12150

Jesse Thaler, Mike Williams, Marisa LaFleur

{"title":"Institute for Artificial Intelligence and Fundamental Interactions (IAIFI): Infusing physics intelligence into artificial intelligence","authors":"Jesse Thaler, Mike Williams, Marisa LaFleur","doi":"10.1002/aaai.12150","DOIUrl":"10.1002/aaai.12150","url":null,"abstract":"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.","PeriodicalId":7854,"journal":{"name":"Ai Magazine","volume":"45 1","pages":"111-116"},"PeriodicalIF":0.9,"publicationDate":"2024-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/aaai.12150","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139959975","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

AgAID Institute—AI for agricultural labor and decision support AgAID 研究所--用于农业劳动和决策支持的人工智能

IF 0.9 4区计算机科学

Ai Magazine Pub Date : 2024-02-16 DOI: 10.1002/aaai.12156

Alan Fern, Margaret Burnett, Joseph Davidson, Janardhan Rao Doppa, Paola Pesantez-Cabrera, Ananth Kalyanaraman

{"title":"AgAID Institute—AI for agricultural labor and decision support","authors":"Alan Fern, Margaret Burnett, Joseph Davidson, Janardhan Rao Doppa, Paola Pesantez-Cabrera, Ananth Kalyanaraman","doi":"10.1002/aaai.12156","DOIUrl":"10.1002/aaai.12156","url":null,"abstract":"The AgAID Institute is a National AI Research Institute focused on developing AI solutions for specialty crop agriculture. Specialty crops include a variety of fruits and vegetables, nut trees, grapes, berries, and different types of horticultural crops. In the United States, the specialty crop industry accounts for a multibillion dollar industry with over 300 crops grown just along the U.S. west coast. Specialty crop agriculture presents several unique challenges: they are labor-intensive, are easily impacted by weather extremities, and are grown mostly on irrigated lands and hence are dependent on water. The AgAID Institute aims to develop AI solutions to address these challenges, particularly in the face of workforce shortages, water scarcity, and extreme weather events. Addressing this host of challenges requires advancing foundational AI research, including spatio-temporal system modeling, robot sensing and control, multiscale site-specific decision support, and designing effective human–AI workflows. This article provides examples of current AgAID efforts and points to open directions to be explored.","PeriodicalId":7854,"journal":{"name":"Ai Magazine","volume":"45 1","pages":"99-104"},"PeriodicalIF":0.9,"publicationDate":"2024-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/aaai.12156","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139961195","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Athena – The NSF AI Institute for Edge Computing 雅典娜 - 国家自然科学基金边缘计算人工智能研究所

IF 0.9 4区计算机科学

Ai Magazine Pub Date : 2024-02-15 DOI: 10.1002/aaai.12147

Yiran Chen, Suman Banerjee, Shaundra Daily, Jeffery Krolik, Hai (Helen) Li, Daniel Limbrick, Miroslav Pajic, Rajashi Runton, Lin Zhong

{"title":"Athena – The NSF AI Institute for Edge Computing","authors":"Yiran Chen, Suman Banerjee, Shaundra Daily, Jeffery Krolik, Hai (Helen) Li, Daniel Limbrick, Miroslav Pajic, Rajashi Runton, Lin Zhong","doi":"10.1002/aaai.12147","DOIUrl":"10.1002/aaai.12147","url":null,"abstract":"The National Science Foundation (NSF) Artificial Intelligence (AI) Institute for Edge Computing Leveraging Next Generation Networks (Athena) seeks to foment a transformation in modern edge computing by advancing AI foundations, computing paradigms, networked computing systems, and edge services and applications from a completely new computing perspective. Led by Duke University, Athena leverages revolutionary developments in computer systems, machine learning, networked computing systems, cyber-physical systems, and sensing. Members of Athena form a multidisciplinary team from eight universities. Athena organizes its research activities under four interrelated thrusts supporting edge computing: Foundational AI, Computer Systems, Networked Computing Systems, and Services and Applications, which constitute an ambitious and comprehensive research agenda. The research tasks of Athena will focus on developing AI-driven next-generation technologies for edge computing and new algorithmic and practical foundations of AI and evaluating the research outcomes through a combination of analytical, experimental, and empirical instruments, especially with target use-inspired research. The researchers of Athena demonstrate a cohesive effort by synergistically integrating the research outcomes from the four thrusts into three pillars: Edge Computing AI Systems, Collaborative Extended Reality (XR), and Situational Awareness and Autonomy. Athena is committed to a robust and comprehensive suite of educational and workforce development endeavors alongside its domestic and international collaboration and knowledge transfer efforts with external stakeholders that include both industry and community partnerships.","PeriodicalId":7854,"journal":{"name":"Ai Magazine","volume":"45 1","pages":"15-21"},"PeriodicalIF":0.9,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/aaai.12147","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139774790","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Building trustworthy NeuroSymbolic AI Systems: Consistency, reliability, explainability, and safety 构建值得信赖的神经符号人工智能系统：一致性、可靠性、可解释性和安全性

IF 0.9 4区计算机科学

Ai Magazine Pub Date : 2024-02-14 DOI: 10.1002/aaai.12149

Manas Gaur, Amit Sheth

{"title":"Building trustworthy NeuroSymbolic AI Systems: Consistency, reliability, explainability, and safety","authors":"Manas Gaur, Amit Sheth","doi":"10.1002/aaai.12149","DOIUrl":"https://doi.org/10.1002/aaai.12149","url":null,"abstract":"Explainability and Safety engender trust. These require a model to exhibit consistency and reliability. To achieve these, it is necessary to use and analyze data and knowledge with statistical and symbolic AI methods relevant to the AI application––neither alone will do. Consequently, we argue and seek to demonstrate that the NeuroSymbolic AI approach is better suited for making AI a trusted AI system. We present the CREST framework that shows how Consistency, Reliability, user-level Explainability, and Safety are built on NeuroSymbolic methods that use data and knowledge to support requirements for critical applications such as health and well-being. This article focuses on Large Language Models (LLMs) as the chosen AI system within the CREST framework. LLMs have garnered substantial attention from researchers due to their versatility in handling a broad array of natural language processing (NLP) scenarios. As examples, ChatGPT and Google's MedPaLM have emerged as highly promising platforms for providing information in general and health-related queries, respectively. Nevertheless, these models remain black boxes despite incorporating human feedback and instruction-guided tuning. For instance, ChatGPT can generate unsafe responses despite instituting safety guardrails. CREST presents a plausible approach harnessing procedural and graph-based knowledge within a NeuroSymbolic framework to shed light on the challenges associated with LLMs.","PeriodicalId":7854,"journal":{"name":"Ai Magazine","volume":"45 1","pages":"139-155"},"PeriodicalIF":0.9,"publicationDate":"2024-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/aaai.12149","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140164349","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

AI2ES: The NSF AI Institute for Research on Trustworthy AI for Weather, Climate, and Coastal Oceanography AI2ES：美国国家科学基金会人工智能研究所，研究天气、气候和沿海海洋学领域可信的人工智能

IF 0.9 4区计算机科学

Ai Magazine Pub Date : 2024-02-14 DOI: 10.1002/aaai.12160

Amy McGovern, Imme Ebert-Uphoff, Elizabeth A. Barnes, Ann Bostrom, Mariana G. Cains, Phillip Davis, Julie L. Demuth, Dimitrios I. Diochnos, Andrew H. Fagg, Philippe Tissot, John K. Williams, Christopher D. Wirz

引用次数: 0