Draft2:Trillion Parameter Consortium
Template:Stub noticeThe 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
DOE's role
The Oak Ridge National Laboratory has joined a global consortium of scientists from federal laboratories, research institutes, academia and industry to address the challenges of building large-scale artificial intelligence systems and advancing trustworthy and reliable AI for scientific discovery.
It’s a logical partnership, as ORNL’s documented mission of developing safe, trustworthy and energy-efficient AI will strengthen the consortium’s goals for responsible AI. Further, the laboratory is home to more than 300 researchers who use AI to tackle challenges of importance to DOE, and it hosts the world’s most powerful supercomputer, Frontier, which was built in part to facilitate energy-efficient and scalable AI-based algorithms and simulations.
ORNL’s AI research thrusts, when deployed alongside these resources, will be critical in assisting the consortium in tackling a number of challenges, including:
- Building 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.
- Incubating, launching and coordinating projects voluntarily to avoid duplication of effort and to maximize the impact of the projects in the broader AI and scientific community.
- Creating 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.
“An integrated and community approach focusing on security, trustworthiness and energy efficiency is crucial to leverage the full potential of AI for scientific discovery and national security,” said Prasanna Balaprakash, ORNL distinguished R&D staff scientist and director of lab’s AI Initiative. “For this reason, ORNL expects to be a critical resource for the consortium going forward, and we look forward to ensuring the future of AI across the scientific spectrum.”[1]
Mission/Objectives
- 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.
Stakeholders
Founding partners
The founding partners of TPC are from the following organizations (listed in organizational alphabetical order with a point-of-contact):
- AI Singapore: Leslie Teo
- Allen Institute For AI: Noah Smith
- AMD: Michael Schulte
- Argonne National Laboratory: Ian Foster
- Barcelona Supercomputing Center: Mateo Valero Cortes
- Brookhaven National Laboratory: Shantenu Jha
- CalTech: Anima Anandkumar
- CEA: Christoph Calvin
- Cerebras Systems: Andy Hock
- CINECA: Laura Morselli
- CSC – IT Center for Science: Per Öster
- CSIRO: Aaron Quigley
- ETH Zürich: Torsten Hoefler
- Fermilab National Accelerator Laboratory: Jim Amundson
- Flinders University: Rob Edwards
- Fujitsu Limited: Koichi Shirahata
- HPE: Nic Dube
- Intel: Koichi Yamada
- Juelich Supercomputing Center: Thomas Lippert
- Kotoba Technologies, Inc.: Jungo Kasai
- LAION: Jenia Jitsev
- Lawrence Berkeley National Laboratory: Stefan Wild
- Lawrence Livermore National Laboratory: Brian Van Essen
- Leibniz Supercomputing Centre: Dieter Kranzlmüller
- Los Alamos National Laboratory: Jason Pruet
- Microsoft: Shuaiwen Leon Song
- National Center for Supercomputing Applications: Bill Gropp
- National Energy Technology Laboratory: Kelly Rose
- National Institute of Advanced Industrial Science and Technology (AIST): Yoshio Tanaka
- National Renewable Energy Laboratory: Juliane Mueller
- National Supercomputing Centre, Singapore: Tin Wee Tan
- NCI Australia: Jingbo Wang
- New Zealand eScience Infrastructure: Nick Jones
- Northwestern University: Pete Beckman
- NVIDIA: Giri Chukkapalli
- Oak Ridge National Laboratory: Prasanna Balaprakash
- Pacific Northwest National Laboratory: Neeraj Kumar
- Pawsey Institute: Mark Stickells
- Princeton Plasma Physics Laboratory: William Tang
- RIKEN: Makoto Taiji
- Rutgers University: Shantenu Jha
- SambaNova: Marshall Choy
- Sandia National Laboratories: John Feddema
- Seoul National University: Jiook Cha
- SLAC National Accelerator Laboratory: Daniel Ratner
- Stanford University: Sanmi Koyejo
- STFC Rutherford Appleton Laboratory, UKRI: Jeyan Thiyagalingam
- Texas Advanced Computing Center: Dan Stanzione
- Thomas Jefferson National Accelerator Facility: Malachi Schram
- Together AI: Ce Zhang
- Tokyo Institute of Technology: Rio Yokota
- Université de Montréal: Irina Rish
- University of Chicago: Rick Stevens
- University of Delaware: Ilya Safro
- University of Illinois Chicago: Michael Papka
- University of Illinois Urbana-Champaign: Lav Varshney
- University of New South Wales: Tong Xie
- University of Tokyo: Kengo Nakajima
- University of Utah: Manish Parashar
- University of Virginia: Geoffrey Fox
Seminars
https://tpc.dev/tpc-seminar-series/
Related links
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External links
Social media
References
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