Artificial Intelligence and Robotics Commons^™

Computing System For Simulation Intelligence, Guangming Tan, Weile Jia, Zhan Wang, Guojun Yuan, En Shao, Ninghui Sun

Bulletin of Chinese Academy of Sciences (Chinese Version)

This study refers computer simulation in scientific research to as scientific simulation. Based on its narrow and broad definitions, this study divides scientific simulation into three stages: numerical computation, simulation intelligence, and science brain. Now, scientific simulation is entering the era of simulation intelligence, i. e., driven by scientific big data and artificial intelligence technology, scientific simulation is shifting from traditional numerical simulation to simulation integrated with artificial intelligence. In order to understand what the right computing system for simulation intelligence is, the design guidelines, basic methods, and key technical problems are discussed.

Large Model-Driven, Human-Computer Collaborative Robotic Ai-Chemist Cloud Facility, Yuanyuan Chong, Shuo Feng, Song Wang, Jun Jiang

Bulletin of Chinese Academy of Sciences (Chinese Version)

At present, chemical science is facing unprecedented opportunities and challenges due to the technological changes brought by artificial intelligence. In order to promote the paradigm shift in chemical research, this study proposes the construction plan of the robotic AI-chemist cloud facility. This system realizes a new paradigm of scientific research by collecting multi-channel data to build a database, developing large scientific models enhanced by chemical knowledge, constructing clusters of robotic facilities, and building an intelligent management decision system, which will dramatically improve the efficiency of scientific research and solve scientific problems in terminal applications. This infrastructure is expected to change …

Ai For Science: Ai Enabled Scientific Facility Transforms Fundamental Research, Xiaokang Yang, Yanyan Xu, Lu Chen, Yunbo Wang, Yue Gao, Jidong Tian, Kai Yu, Yaohui Jin, Hong Mei

Bulletin of Chinese Academy of Sciences (Chinese Version)

In recent years, artificial intelligence (AI) has achieved numerous disruptive breakthroughs in frontier scientific and technological fields, such as AlphaFold2 for protein structure prediction, intelligent control of nuclear fusion, and drug design for COVID-19. These achievements indicate that AI for Science is becoming a new paradigm in research. To achieve fundamental scientific innovation and major technological breakthroughs in the era of intelligence, two core issues should be addressed: 1) how to harness the generality and creativity of the new-generation of AI, especially generative AI and large language models (LLMs), to promote the formation of new paradigms; 2) how to empower …

Ai4r: The Fifth Scientific Research Paradigm, Guojie Li

Bulletin of Chinese Academy of Sciences (Chinese Version)

This article refers to “AI for Research（AI4R）” as the fifth research paradigm and summarizes its characteristics, including: (1) the fully integration of artificial intelligence into various scientific and technology researches; (2) machine intelligence has become an integral part of scientific research; (3) effectively handles the combinatorial explosion problem with high computational complexity; (4) probability and statistical models play a greater role in scientific research; (5) realize the integration of four existing research paradigms, cross disciplinary cooperation has become the mainstream research method; (6) scientific research relies more on large research platforms characterized by large models. This article points out that …

Ai Helps To Establish A New Paradigm For Scientific Research, Weinan E

Bulletin of Chinese Academy of Sciences (Chinese Version)

The main purpose of scientific research is to discover fundamental principles and solve practical problems. Although tremendous progress has been made on both fronts, the lack of effective tools and efficient organizational structure still stands as the main bottleneck for scientific progress. The rapid development of artificial intelligence (AI) offers a new possibility. In recent years, deep learning has had an impressive performance, both in helping to solve fundamental scientific problems and in improving the effectiveness of scientific research tools. A new set of infrastructure is emerging, leading us to a new paradigm, the“Android paradigm”, for doing scientific research.

Ai For Technology: Applied Practices And Future Perspectives Of Technological Intelligence In High Tech Areas, Yunji Chen, Qi Guo

Bulletin of Chinese Academy of Sciences (Chinese Version)

As the core of the fifth research paradigm, AI for Science has been widely used in multiple research fields of natural sciences and high technologies. In contrast to that the application of AI in natural sciences mainly focuses on discovering new theories, principles, and laws, the application of AI in high technologies mainly focuses on creating new plans, tools, and products, in order to resolve concrete problems in related fields. This study first summarizes the typical characteristics and scientific problems of the application of AI in high technologies, i.e., AI for Technology, and then introduces a successful case study of …

A New Paradigm Of Life Science Research Driven By Artificial Intelligence, Xin Li, Hanchao Yu

Bulletin of Chinese Academy of Sciences (Chinese Version)

The rapid development of biotechnology and information technology has brought life sciences into a new era of data explosion. The traditional life science research paradigm struggles to reveal the fundamental rules of complex biological systems from rapidly growing biological big data. As artificial intelligence continues to achieve disruptive breakthroughs in life science, a new paradigm driven by AI is emerging. This study delves into typical examples of life science research driven by AI, proposes the concept and key elements of the new life science research paradigm, elaborates on the cutting-edge of life science research under this new paradigm, and discusses …

Building New Paradigm Of Digital Intelligence Security For New Development Pattern, Xiaoguang Yang, Yang Wu, Xingwei Zhang, Xiaolong Zheng

Bulletin of Chinese Academy of Sciences (Chinese Version)

After the 20th National Congress of the Communist Party of China, China has entered a new era of development. Simultaneously, the rapid advancement and extensive application of artificial intelligent technologies have activated a new wave of economic potential and brought about new security challenges to socioeconomic development. This study firstly analyzes the characteristics of the new development pattern in global contexts, and examines the risks and challenges of digital intelligence security (DIS) under the new pattern, encompassing technological security and personal security at the micro-level, as well as economic security, social security, and cultural security at the macro-level. Based on …

Strategic Research On Information Technology Promoting National Governance Modernization—Review On The S70th Xiangshan Science Conferences, Chao Zhang, Weiyu Duan, Kaihua Chen, Xiaoguang Yang, Yuntao Long

Bulletin of Chinese Academy of Sciences (Chinese Version)

This study systematically summarizes the reports and speeches of the S70th Xiangshan Science Conferences on the theme of “Strategic Research on Information Technology Promoting the National Governance Modernization” and summarizes the consensus of the conference in the following three aspects. (1) Important progress and achievements have been made in the four typical areas, i.e., smart justice, internet governance, data governance, and emergency management. (2) Using information technology to promote the modernization of national governance is confronted with unprecedented opportunities and challenges. And (3) it is necessary to take a series of effective measures to promote information technology to facilitate the …

Analysis And Reflections On Key Platform Facilities Construction Of Global Biomanufacturing Industry, Xiaoyan Wu, Fang Chen, Yaoying Shan, Anjing Lu

Bulletin of Chinese Academy of Sciences (Chinese Version)

Biomanufacturing, an emerging production method, is becoming a significant trend in global economic development and has garnered widespread international attention. Platform facilities are vital to the biomanufacturing industry’s development, serving as both the foundation for technological innovation and the bridge between research outcomes and practical applications. This study analyzes three key types of platform facilities and their operational mechanisms: technology innovation platforms (exemplified by the U.S. Agile BioFoundry), pilot-scale platforms (represented by European Bio Base Europe Pilot Plant), and industry incubation platforms (illustrated by the UK SynbiCITE). Drawing from these successful examples and examining China’s current platform infrastructure, this paper …

Ai-Enhanced Detection Of Clinically Relevant Structural And Functional Anomalies In Mri: Traversing The Landscape Of Conventional To Explainable Approaches, Pegah Khosravi, Saber Mohammadi, Fatemeh Zahiri, Masoud Khodarahmi, Javad Zahiri

Publications and Research

Anomaly detection in medical imaging, particularly within the realm of magnetic resonance imaging (MRI), stands as a vital area of research with far-reaching implications across various medical fields. This review meticulously examines the integration of artificial intelligence (AI) in anomaly detection for MR images, spotlighting its transformative impact on medical diagnostics. We delve into the forefront of AI applications in MRI, exploring advanced machine learning (ML) and deep learning (DL) methodologies that are pivotal in enhancing the precision of diagnostic processes. The review provides a detailed analysis of preprocessing, feature extraction, classification, and segmentation techniques, alongside a comprehensive evaluation of …

Enabling The Clinical Application Of Artificial Intelligence In Genomics: A Perspective Of The Amia Genomics And Translational Bioinformatics Workgroup, Robert R Freimuth, David B Everman, Derek C Walton, Scott P Mcgrath, Dominick J Lemas, Panayiotis V Benos, Alexander V Alekseyenko, Qianqian Song, Ece Gamsiz Uzun, Casey Overby Taylor, Alper Uzun, Thomas Nate Person, Nadav Rappoport, Zhongming Zhao, Marc S Williams

Faculty, Staff and Student Publications

OBJECTIVE: Given the importance AI in genomics and its potential impact on human health, the American Medical Informatics Association-Genomics and Translational Biomedical Informatics (GenTBI) Workgroup developed this assessment of factors that can further enable the clinical application of AI in this space.

PROCESS: A list of relevant factors was developed through GenTBI workgroup discussions in multiple in-person and online meetings, along with review of pertinent publications. This list was then summarized and reviewed to achieve consensus among the group members.

CONCLUSIONS: Substantial informatics research and development are needed to fully realize the clinical potential of such technologies. The development of …

Reusing Deep Learning Models: Challenges And Directions In Software Engineering, James C. Davis, Purvish Jajal, Wenxin Jiang, Taylor R. Schorlemmer, Nicholas Synovic, George K. Thiruvathukal

Computer Science: Faculty Publications and Other Works

Deep neural networks (DNNs) achieve state-of-the-art performance in many areas, including computer vision, system configuration, and question-answering. However, DNNs are expensive to develop, both in intellectual effort (e.g., devising new architectures) and computational costs (e.g., training). Re-using DNNs is a promising direction to amortize costs within a company and across the computing industry. As with any new technology, however, there are many challenges in re-using DNNs. These challenges include both missing technical capabilities and missing engineering practices. This vision paper describes challenges in current approaches to DNN re-use. We summarize studies of re-use failures across the spectrum of re-use techniques, …

Detection And Classification Of Sporadic E Using Convolutional Neural Networks, J. A. Ellis, Daniel J. Emmons, M. B. Cohen

Faculty Publications

In this work, convolutional neural networks (CNN) are developed to detect and characterize sporadic E (E_s), demonstrating an improvement over current methods. This includes a binary classification model to determine if Es is present, followed by a regression model to estimate the Es ordinary mode critical frequency (foEs), a proxy for the intensity, along with the height at which the Es layer occurs (hEs). Signal-to-noise ratio (SNR) and excess phase profiles from six Global Navigation Satellite System (GNSS) radio occultation (RO) missions during the years 2008–2022 are used as the inputs of the model. Intensity (foEs) and the …

Meet Scite, “Chatgpt For Research”, A New Artificial Intelligence (Ai) Tool Available Via Clemson Libraries, Jennifer Groff, Shelby Carroll

Clemson Teaching Excellence Conference 2024: Teaching in the Age of AI

No abstract provided.

Hiccups And Hallucinations: Critically Engaging Ai In The Design Classroom, Drew Sisk

Clemson Teaching Excellence Conference 2024: Teaching in the Age of AI

No abstract provided.

The Use And Misuse Of Generative Ai For Photos And Imagery, Erica B. Walker

Clemson Teaching Excellence Conference 2024: Teaching in the Age of AI

No abstract provided.

Academic Ethics In Ai-Assisted Writing: A Writing Center-Informed Approach, John Falter

Clemson Teaching Excellence Conference 2024: Teaching in the Age of AI

No abstract provided.

Navigating Anxiety And Activity: Generative Ai And Writing Support, Chelsea J. Murdock

Clemson Teaching Excellence Conference 2024: Teaching in the Age of AI

No abstract provided.

Using Generative Artificial Intelligence For Engaged Student Learning, Janice G. Lanham, Charlotte Branyon

Clemson Teaching Excellence Conference 2024: Teaching in the Age of AI

No abstract provided.