The National Science Foundation (NFS) announces its plans to conduct research on the use of artificial intelligence for understanding dynamic systems under the leadership of the University of Washington (UW).
Under the new initiative of NSF, eleven new National Artificial Intelligence Research Institutes will be built across forty states. NSF has already received funding of $220 million for this purpose.
Earlier this year, NSF had collaborated with Amazon Web Services for its Fairness in Artificial Intelligence Program.
The research program will focus on the fundamentals of artificial intelligence and machine learning and develop real-time learning applications for controlling complex dynamic systems.
An associate professor at the University of Washington, Steve Brunton, said, “We literally live and breathe inside of a working fluid, and so do nearly all of our machines. But because of the multiscale complexity of the fluid, which involves a cascade of increasingly smaller eddies, we still have an incredibly hard time predicting what fluids will do outside of idealized and controlled settings.”
He further added that they want to explore the possibility of training machine learning and artificial intelligence systems that would enable them to learn partially known or unknown physics concepts.
The collaborative research will develop data-enabled artificial intelligence solutions to tackle several challenges in the field of science and engineering. Apart from research, the collaboration would also train future researchers by providing them the necessary support and assistance.
They intend to tie up with high schools and offer artificial intelligence projects to educate students and would also actively recruit fresh college graduates from underrepresented regions of the United States to train them.
An applied mathematics professor at UW, Nathan Kutz, said, “Importantly, we will provide AI ethics training for all involved in the institute. We will also make this training available to the community at large, thus enforcing a disciplined approach to thinking about AI.”