Adam Klivans, Alexandros G. Dimakis, Kristen Grauman, Jonathan I. Tamir, Daniel J. Diaz, Karen Davidson
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Institute for Foundations of Machine Learning (IFML): Advancing AI systems that will transform our world
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:
期刊介绍:
AI Magazine publishes original articles that are reasonably self-contained and aimed at a broad spectrum of the AI community. Technical content should be kept to a minimum. In general, the magazine does not publish articles that have been published elsewhere in whole or in part. The magazine welcomes the contribution of articles on the theory and practice of AI as well as general survey articles, tutorial articles on timely topics, conference or symposia or workshop reports, and timely columns on topics of interest to AI scientists.