RSS feed source: National Science Foundation

Synopsis

The NSF SBIR/STTR programs provide non-dilutive funds for use-inspired research and development (R&D) of unproven, leading-edge, technology innovations that address societal challenges. By investing federal research and development funds into startups and small businesses, NSF helps build a strong national economy and stimulates the creation of novel products, services, and solutions in the private sector; strengthens the role of small business in meeting federal research and development needs; increases the commercial application of federally-supported research results; and develops and increases the U.S. workforce, especially by fostering and encouraging participation by socially and economically-disadvantaged and women-owned small businesses.

NSF seeks unproven, leading-edge technology innovations that demonstrate the following characteristics:

The innovations are underpinned and enabled by a new scientific discovery or meaningful engineering innovation. The innovations still require intensive technical research and development to be fully embedded in a reliable product or service. The

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RSS feed source: National Science Foundation

On July 10, 2025, NSF issued an Important Notice providing updates to the agency’s research security policies, including a research security training requirement, Malign Foreign Talent Recruitment Program annual certification requirement, prohibition on Confucius institutes and an updated FFDR reporting and submission timeline.

Click this link to continue reading the article on the source website.

RSS feed source: National Science Foundation

On July 10, 2025, NSF issued an Important Notice providing updates to the agency’s research security policies, including a research security training requirement, Malign Foreign Talent Recruitment Program annual certification requirement, prohibition on Confucius institutes and an updated FFDR reporting and submission timeline.

Click this link to continue reading the article on the source website.

RSS feed source: National Science Foundation

Synopsis

Machine Learning and Artificial Intelligence (AI) are enabling extraordinary scientific breakthroughs in fields ranging from protein folding, natural language processing, drug synthesis, and recommender systems to the discovery of novel engineering materials and products. These achievements lie at the confluence of mathematics, statistics, engineering and computer science, yet a clear explanation of the remarkable power and also the limitations of such AI systems has eluded scientists from all disciplines. Critical foundational gaps remain that, if not properly addressed, will soon limit advances in machine learning, curbing progress in artificial intelligence. It appears increasingly unlikely that these critical gaps can be surmounted with increased computational power and experimentation alone. Deeper mathematical understanding is essential to ensuring that AI can be harnessed to meet the future needs of society and enable broad scientific discovery, while forestalling the unintended consequences of a disruptive technology.  

The National

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