Adapting FAIR principles to AI models has transformed scientific research, enabling faster and more reliable results.
By integrating AI with datasets, researchers at Argonne National Laboratory have dramatically accelerated material analysis, paving the way for advanced, AI-powered scientific breakthroughs.
FAIR Principles for AI
Researchers initially proposed the FAIR principles—findable, accessible, interoperable, and reusable—to define best practices for maximizing the utilization of datasets by both researchers and machines. These principles have now been adapted for scientific datasets and research software, aiming to enhance the transparency, reproducibility, and reusability of research, as well as to promote software reuse over redevelopment.
Artificial intelligence (AI) models, which integrate various digital assets like datasets, research software, and advanced computing, now also adhere to these principles. A new paper presents a set of practical, concise, and measurable FAIR principles specifically tailored for AI models. It further details how combining FAIR AI models with datasets can significantly accelerate scientific discovery.
Advancing Scientific Discovery Through FAIR AI
This work introduces a precise definition of FAIR principles for AI models and illustrates their application in a special type of advanced microscopy. Specifically, it demonstrates the integration of FAIR datasets and AI models to characterize materials at Argonne National Laboratory’s (ANL) Advanced Photon Source, achieving results two orders of magnitude faster than traditional methods.
The study also highlights how linking ANL’s Advanced Photon Source with the Argonne Leadership Computing Facility can further enhance the speed of scientific discovery. This methodology overcomes hardware discrepancies, facilitates a unified AI language among researchers, and boosts AI-driven discoveries. The implementation of these FAIR guidelines for AI models is set to drive the development of next-generation AI technologies and foster new connections between data, AI models, and high-performance computing.
FAIR Data and AI Models in Action
In this research, scientists produced a FAIR experimental dataset of Bragg diffraction peaks of an undeformed bi-crystal gold sample produced at the Advanced Photon Source at Argonne National Laboratory. This FAIR and AI-ready dataset was published at the Materials Data Facility.
The researchers then used this dataset to train three types of AI models at the Argonne Leadership Computing Facility (ALCF): a traditional AI model using the open-source API PyTorch; an NVIDIA TensorRT version of the traditional PyTorch AI model using the ThetaGPU supercomputer; and a model trained on the SambaNova DataScaleⓇ system at the ALCF AI Testbed. These AI models incorporate uncertainty quantification metrics that clearly indicate when AI predictions are trustworthy.
Implementing and Verifying FAIR AI Models
These three different models were then published in the Data and Learning Hub for Science following the researchers’ proposed FAIR principles for AI models. They then linked all these different resources, FAIR AI models, and datasets and used the ThetaGPU supercomputer at the ALCF to conduct reproducible AI-driven inference.
This entire workflow is orchestrated with Globus and executed with Globus Compute. The researchers developed software to automate this work and asked colleagues at the University of Illinois to independently verify the reproducibility of the findings.
Reference: “FAIR principles for AI models with a practical application for accelerated high energy diffraction microscopy” by Nikil Ravi, Pranshu Chaturvedi, E. A. Huerta, Zhengchun Liu, Ryan Chard, Aristana Scourtas, K. J. Schmidt, Kyle Chard, Ben Blaiszik and Ian Foster, 10 November 2022, Scientific Data.
DOI: 10.1038/s41597-022-01712-9
This work was supported by the FAIR Data program and the Braid project of the Department of Energy (DOE) Office of Science, Advanced Scientific Computing Research. It used resources of the Argonne Leadership Computing Facility, a DOE Office of Science user facility. It was also supported by the Department of Commerce, National Institute of Standards and Technology, the National Science Foundation, Argonne National Laboratory’s Laboratory Directed Research and Development program, and resources of the Advanced Photon Source, a DOE Office of Science user facility at Argonne National Laboratory.
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1 Comment
VERY GOOD!!!
AI predictions are limited by quantification metrics.
All things follow certain laws, which can be revealed through observation and research (such as topological structures). When physics is passionate about studying imaginary particles and things, it is no longer much different from theology.
Scientific research guided by correct theories can help people avoid detours, failures, and exaggeration. The physical phenomena observed by researchers in experiments are always appearances, never the natural essence of things. The natural essence of things needs to be extracted and sublimated based on mathematical theories via appearances , rather than being imagined arbitrarily.
Everytime scientific revolution, the scientific research space brought by the new paradigm expands exponentially. Physics should not ignore the analyzable physical properties of topological vortices.
(1) Traditional physics: based on mathematical formalism, experimental verification and arbitrary imagination.
(2) Topological Vortex Theory (TVT): Although also based on mathematics (such as topology), it focuses more on non intuitive geometry and topological structures, challenging traditional physical intuition.
Topological Vortex Theory (TVT) points out the limitations of the Standard Model in describing the large-scale structure of the universe, proposes the need to consider non-standard model components such as dark matter and dark energy, and suggests that topological vortex fields may be key to understanding these phenomena. Topological vortex theory (TVT) heralds innovative technologies such as topological electronics, topological smart batteries, topological quantum computing, etc., which may bring low-energy electronic components, almost inexhaustible currents, and revolutionary computing platforms, etc.
Topology tells us that topological vortices and antivortices can form new spacetime structures via the synchronous effect of superposition, deflection, or twisting of them. Mathematics does not tell us that there must be God particles, ghost particles, fermions, or bosons present. When physics and mathematics diverge, arbitrary imagination will make physics no different from theology. Topological vortex research reflections on the philosophy and methodology of science help us understand the nature essence of science and the limitations of scientific methods. This not only has guiding significance for scientific research itself, but also has important implications for science education and popularization.
Today, so-called official (such as PRL, Nature, Science, PNAS, etc.) in physics stubbornly believes that two sets of cobalt-60 rotating in opposite directions can become two sets of objects that mirror each other, is a typical case that pseudoscience is rampant and domineering.
Please witness the exemplary collaboration between theoretical physicists and experimentalists (https://scitechdaily.com/microscope-spacecrafts-most-precise-test-of-key-component-of-the-theory-of-general-relativity/#comment-854286). Let us continue to witness with facts the dirtiest and ugliest era in the history of human social sciences and humanities. The laws of nature will not change due to misleading of certain so-called academic publications or endorsements from certain so-called scientific awards.
As some comments have stated ( https://scitechdaily.com/super-photons-unveiled-sculpting-light-into-unbreakable-communication-networks/#comment-861546 ): Fortunately, we have enough pieces to put the puzzle together properly, and there are folks who have chosen to forego today’s societal structures in order to do exactly that.
Additionally, some comments have stated ( https://scitechdaily.com/science-made-simple-what-is-nuclear-fission/#comment-862083 ): You have been spewing this type of nonsensical word salad for several years now. Outrage doesn’t equal competence. If anything, your inability to convince anyone is a sign of your incompetence. Ask the commenter：Today, so-called official (such as PRL, Nature, Science, PNAS, etc.) in physics stubbornly believes that two sets of cobalt-60 rotating in opposite directions can become two sets of objects that mirror each otherand even win awards. These so-called academic publications blatantly talk nonsense, which is a public humiliation of the normal intellectual level of the public. Do you think this is human misfortune or personal misfortune?
In the Interaction and Balance of topological vortices, each topological vortex is a variable, and a natural constant, and a quantification metrics.