Ai Magazine最新文献

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.

Optimization is a universal quest, reflecting the basic human need to do better. Improved optimizations of energy-efficiency, safety, robustness, and other criteria in engineered systems would bring incalculable societal benefits. But, fundamental challenges of scale and complexity keep many such real-world optimization needs beyond reach. This article describes The Institute for Learning-enabled Optimization at Scale (TILOS), an NSF AI Research Institute for Advances in Optimization that aims to overcome these challenges in three high-stakes use domains: chip design, communication networks, and contextual robotics. TILOS integrates foundational research, translation, education, and broader impacts toward a new nexus of optimization, AI, and data-driven learning. We summarize central challenges, early progress, and futures for the institute.

The National AI Institute for Adult Learning and Online Education (AI-ALOE) develops AI learning and teaching assistants to enhance the proficiency of adult reskilling and upskilling, and thereby transform workforce development. The AI assistants both address known problems in online education for reskilling/upskilling and help personalize adult learning for workforce development. AI-ALOE develops new AI models and techniques for self-explanation, machine teaching, and mutual theory of mind to make the AI assistants usable, learnable, teachable, and scalable. AI-ALOE is also developing a data architecture for deploying and evaluating the AI assistants, collecting and analyzing data, and personalizing learning at scale.

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.

It is sometimes difficult to evaluate progress in Generative AI, that is, image generation and large language models. This may be because they represent a paradigm shift in AI, and the traditional ways of developing, evaluating, understanding, and deploying AI systems no longer apply. Instead, we need to develop new such approaches, possibly by extending those currently in use in cognitive neuroscience and psychology. In this manner, a new AI paradigm can be created, providing a significant leap in AI research and practice.

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.

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.

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.

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.

The NSF AI Institute for Research on Trustworthy AI in Weather, Climate, and Coastal Oceanography (AI2ES) focuses on creating trustworthy AI for a variety of environmental and Earth science phenomena. AI2ES includes leading experts from AI, atmospheric and ocean science, risk communication, and education, who work synergistically to develop and test trustworthy AI methods that transform our understanding and prediction of the environment. Trust is a social phenomenon, and our integration of risk communication research across AI2ES activities provides an empirical foundation for developing user-informed, trustworthy AI. AI2ES also features activities to broaden participation and for workforce development that are fully integrated with AI2ES research on trustworthy AI, environmental science, and risk communication.