US National Science Foundation commits to long-term investment in artificial intelligence

According to the updated AI Strategic Plan, much of the AI research to date has been specialized around specific tasks, and longer-term investments are needed to expand and broaden AI into “systems that exhibit the flexibility and versatility of human intelligence in a broad range of cognitive domai...

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Bibliographic Details
Published inMRS bulletin Vol. 45; no. 1; pp. 15 - 16
Main Author Malik, Jennifer A. Nekuda
Format Journal Article
LanguageEnglish
Published Cham Springer International Publishing 2020
Springer Nature B.V
Subjects
Algorithms
Applied and Technical Physics
Artificial intelligence
Characterization and Evaluation of Materials
Data transmission
Departments
Energy Materials
Funding
Grants
Machine learning
Materials Engineering
Materials Science
Nanotechnology
R&D
Research & development
Research centers
Researchers
New York
United States > US
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Summary:According to the updated AI Strategic Plan, much of the AI research to date has been specialized around specific tasks, and longer-term investments are needed to expand and broaden AI into “systems that exhibit the flexibility and versatility of human intelligence in a broad range of cognitive domains.” According to Maruyama, materials researchers can readily provide “data streams of experimental and simulated data,” which Reyes says could be used to determine how well “problem-agnostic ML/AI methods” can adapt to a variety of data types and sources. According to Maruyama, “Autonomous Research systems will lead to an exponential explosion in research progress, akin to a Moore’s Law for the speed of research.”
ISSN:0883-7694
1938-1425
DOI:10.1557/mrs.2020.8