Artificial intelligence (AI) and machine learning (ML) models can solve well-specified problems, like automatically diagnosing disease or grading student essays, at scale. But applications of AI and ML for major social and scientific problems are often constrained by a lack of high-quality, publicly available data—the foundation on which AI and ML algorithms are built.
The Biden-Harris Administration should launch a multi-agency initiative to coordinate the academic, industry, and government research community to support the identification and development of datasets for applications of AI and ML in domain-specific, societally valuable contexts. The initiative would include activities like generating ideas for high-impact datasets, linking siloed data into larger and more useful datasets, making existing datasets easier to access, funding the creation of real-world testbeds for societally valuable AI and ML applications, and supporting public-private partnerships related to all of the above.
“The awesome thing is that folks are really interested in a conversion to clean energy and what they can do to support the Tribe. It’s really fun to go out there and see that people want to move in that direction.”
Despite significant advances in scientific tools and methods, the traditional, labor-intensive model of scientific research in materials discovery has seen little innovation.
Community navigator programs can provide much-needed capacity combined with deep place-based knowledge to create local champions with expertise in accessing federal funding.
From the forests of Western Massachusetts, to the desert mountains of Arizona, to the frosty fields of Wisconsin, Dr. Adria Brooks has made a career out of teaching others why they should care about clean energy.