Cloud Hosting

A global consortium of scientists from federal laboratories, research institutes, academia, and industry has formed to address the challenges of building large-scale artificial intelligence (AI) systems and advancing trustworthy and reliable AI for scientific discovery.

The Trillion Parameter Consortium (TPC) brings together teams of researchers engaged in creating large-scale generative AI models to address key challenges in advancing AI for science. These challenges include developing scalable model architectures and training strategies, organizing, and curating scientific data for training models; optimizing AI libraries for current and future exascale computing platforms; and developing deep evaluation platforms to assess progress on scientific task learning and reliability and trust.

At our laboratory and at a growing number of partner institutions around the world, teams are beginning to develop frontier AI models for scientific use and are preparing enormous collections of previously untapped scientific data for training. Rick Stevens, Argonne associate laboratory director for computing, environment and life sciences

Toward these ends, TPC will:

The consortium has formed a dynamic set of foundational work areas addressing three facets of the complexities of building large-scale AI models:

TPC aims to provide the community with a venue in which multiple large model-building initiatives can collaborate to leverage global efforts, with flexibility to accommodate the diverse goals of individual initiatives. TPC includes teams that are undertaking initiatives to leverage emerging exascale computing platforms to train LLMs or alternative model architectures on scientific research including papers, scientific codes, and observational and experimental data to advance innovation and discoveries.

Trillion parameter models represent the frontier of large-scale AI with only the largest commercial AI systems currently approaching this scale.

Training LLMs with this many parameters requires exascale class computing resources, such as those being deployed at several U.S. Department of Energy (DOE) national laboratories and multiple TPC founding partners in Japan, Europe, and elsewhere. Even with such resources, training a state-of-the-art one trillion parameter model will require months of dedicated timeintractable on all but the largest systems. Consequently, such efforts will involve large, multi-disciplinary, multi-institutional teams. TPC is envisioned as a vehicle to support collaboration and cooperative efforts among and within such teams.

At our laboratory and at a growing number of partner institutions around the world, teams are beginning to develop frontier AI models for scientific use and are preparing enormous collections of previously untapped scientific data for training, said Rick Stevens, associate laboratory director of computing, environment and life sciences at DOEs Argonne National Laboratory and professor of computer science at the University of Chicago. We collaboratively created TPC to accelerate these initiatives and to rapidly create the knowledge and tools necessary for creating AI models with the ability to not only answer domain-specific questions but to synthesize knowledge across scientific disciplines.

The founding partners of TPC are from the following organizations (listed in organizational alphabetical order, with a point-of-contact):

TPC contact: Charlie Catlett

Learn more at tpc.dev.

Follow this link:

New international consortium formed to create trustworthy and ... - Argonne National Laboratory