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 largescale generative AI models to address key challenges in advancing AI for science. These challenges include developing scalable model architectures and training strategies, organising, and curating scientific data for training models; optimising 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.
Toward these ends, TPC will:
- Build an open community of researchers interested in creating state-of-the-art large-scale generative AI models aimed broadly at advancing progress on scientific and engineering problems by sharing methods, approaches, tools, insights, and workflows.
- Incubate, launch, and coordinate projects voluntarily to avoid duplication of effort and to maximize the impact of the projects in the broader AI and scientific community.
- Create a global network of resources and expertise to facilitate the next generation of AI and bring together researchers interested in developing and using large-scale AI for science and engineering.
The consortium has formed a dynamic set of foundational work areas addressing three facets of the complexities of building large-scale AI models:
- Identifying and preparing high-quality training data, with teams organised around the unique complexities of various scientific domains and data sources.
- Designing and evaluating model architectures, performance, training, and downstream applications.
- Developing crosscutting and foundational capabilities such as innovations in model evaluation strategies with respect to bias, trustworthiness, and goal alignment, among others.
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 Large Language Models (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, Australia, and elsewhere. Even with such resources, training a state-of-the-art one trillion parameter model will require months of dedicated time —intractable 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 DOE’s 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 synthesise knowledge across scientific disciplines.”
“Joining the Trillion Parameter Consortium signifies NCI Australia's strategic commitment to unlocking the full potential of AI and machine learning, transcending boundaries, and ushering in a new era of innovation and discovery on an unprecedented scale,” said NCI’s director Professor Sean Smith.
Join NCI and other partners at the Trillion Parameter Consortium (TPC) tutorial at SupercomputingAsia2024 in Sydney to delve into the forefront of this ground-breaking technology, exploring the limitless possibilities that arise from handling unprecedented data scale and complexity. Organised by Charlie Catlett and Arvind Ramanathan of Argonne National Laboratory along with Prasanna Balaprakash of Oak Ridge National Laboratory, this tutorial offers a unique opportunity to gain hands-on experience and insights into managing and harnessing the immense potential of the TPC.
Explore TPC and join the community at https://tpc.dev