Ai Magazine最新文献

Earlier this year, OpenAI released their GPTs framework, allowing users to set up Large Language Model (LLM)-based personas, orchestrate them into a workflow and even offering their AI apps within an app store. This is the latest, and maybe the easiest to set up, in a string of agent-based LLM orchestration platforms in the past year, harkening a new age of agent-based engineering. But, like most breakthroughs, this one is also rooted in many years of research, and the reason the world is paying attention to it now is that, thanks to Generative AI and Large Language Models, we finally have artificial agents that are useful enough to scale to more serious problems.

I argue that ensuring artificial intelligence (AI) retains alignment with human values over time is critical yet understudied. Most research focuses on static alignment, neglecting crucial retention dynamics enabling stability during learning and autonomy. This paper elucidates limitations constraining provable retention, arguing key gaps include formalizing dynamics, transparency of advanced systems, participatory scaling, and risks of uncontrolled recursive self-improvement. I synthesize technical and ethical perspectives into a conceptual framework grounded in control theory and philosophy to analyze dynamics. I argue priorities should shift towards capability modulation, participatory design, and advanced modeling to verify enduring alignment. Overall, I argue that realizing AI safely aligned throughout its lifetime necessitates translating principles into formal methods, demonstrations, and systems integrating technical and humanistic rigor.

The Institute for Foundations of Machine Learning (IFML) focuses on core foundational tools to power the next generation of machine learning models. Its research underpins the algorithms and data sets that make generative artificial intelligence (AI) more accurate and reliable. Headquartered at The University of Texas at Austin, IFML researchers collaborate across an ecosystem that spans University of Washington, Stanford, UCLA, Microsoft Research, the Santa Fe Institute, and Wichita State University. Over the past year, we have witnessed incredible breakthroughs in AI on topics that are at the heart of IFML's agenda, such as foundation models, LLMs, fine-tuning, and diffusion with game-changing applications influencing almost every area of science and technology. In this article, we seek to highlight seek to highlight the application of foundational machine learning research on key use-inspired topics:

Artificial intelligence (AI) has the potential for vast societal and economic gain; yet applications are developed in a largely ad hoc manner, lacking coherent, standardized, modular, and reusable infrastructures. The NSF-funded Intelligent CyberInfrastructure with Computational Learning in the Environment AI Institute (“ICICLE”) aims to fundamentally advance edge-to-center, AI-as-a-Service, achieved through intelligent cyberinfrastructure (CI) that spans the edge-cloud-HPC computing continuum, plug-and-play next-generation AI and intelligent CI services, and a commitment to design for broad accessibility and widespread benefit. This design is foundational to the institute's commitment to democratizing AI. The institute's CI activities are informed by three high-impact domains: animal ecology, digital agriculture, and smart foodsheds. The institute's workforce development and broadening participation in computing efforts reinforce the institute's commitment to democratizing AI. ICICLE seeks to serve as the national nexus for AI and intelligent CI, and welcomes engagement across its wide set of programs.

Over 13 million Americans aged 65 and older are currently living with a diagnosis of mild cognitive impairment (MCI), a common precursor to dementia. These individuals largely rely on a network of informal caregivers—family, friends, and community members—who work together with professional healthcare and social service providers to provide care and support in home settings. The AI-CARING Institute contributes foundational AI research focused on developing personalized collaborative AI systems that improve the quality of life and independence of aging adults living at home.

Our food system is complex, multifaceted, and in need of an upgrade. Population growth, climate change, and socioeconomic disparities are some of the challenges that create a systemic threat to its sustainability and capacity to address the needs of an evolving planet. The mission of the AI Institute of Next Generation Food Systems (AIFS) is to leverage the latest advances in AI to help create a more sustainable, efficient, nutritious, safe, and resilient food system. Instead of using AI in isolation, AIFS views it as the connective tissue that can bring together interconnected solutions from farm to fork. From guiding molecular breeding and building autonomous robots for precision agriculture, to predicting pathogen outbreaks and recommending personalized diets, AIFS projects aspire to pave the way for infrastructure and systems that empower practitioners to build the food system of the next generation. Workforce education, outreach, and ethical considerations related to the emergence of AI solutions in this sector are an integral part of AIFS with several collaborative activities aiming to foster an open dialogue and bringing closer students, trainees, teachers, producers, farmers, workers, policy makers, and other professionals.

The AIFARMS Artificial Intelligence for Future Agricultural Resilience, Management, and Sustainability national AI institute brings together over 40 world-class AI and agriculture researchers, with the common mission to develop foundational advances in AI and use them to ensure that future agriculture is environmentally friendly, sustainable, affordable, and accessible to diverse farming communities. Since its establishment in 2020, AIFARMS has advanced the state of the art in autonomous farming, cover crop planting, machine learning for improved outcomes from remote sensing, dynamic estimation of yield loss from weeds, and livestock management. The institute has prioritized the creation and utilization of high-quality, openly available data sets for advancing foundational AI and tackling agricultural challenges. AIFARMS leverages a close partnership between UIUC and Tuskegee University to build programming for a skilled and diverse next-generation workforce in digital agriculture. We are expanding the reach of AIFARMS outside of the current partners to collaborate with national AI institutions and international partners.

The Institute for Student-AI Teaming (iSAT) addresses the foundational question: how to promote deep conceptual learning via rich socio-collaborative learning experiences for all students?—a question that is ripe for AI-based facilitation and has the potential to transform classrooms. We advance research in speech, computer vision, human-agent teaming, computer-supported collaborative learning, expansive co-design, and the science of broadening participation to design and study next generation AI technologies (called AI Partners) embedded in student collaborative learning teams in coordination with teachers. Our institute ascribes to theoretical perspectives that aim to create a normative environment of widespread engagement through responsible design of technology, curriculum, and pedagogy in partnership with K–12 educators, racially diverse students, parents, and other community members.

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.