The National Science Foundation Convergence Accelerator addresses national-scale societal challenges through use-inspired convergence research. Leveraging a convergence approach the Convergence Accelerator builds upon basic research and discovery to make timely investments to strengthen the Nations innovation ecosystem associated with several key R&D priority areas and practices to include the coronavirus disease 2019, harnessing the data revolution, the future of work, and quantum technology. Artificial Intelligence is a key underlying theme across all of these areas.

Knowledge Graphs (KGs) represent real-world noisy raw information in a structured form, capturing relationships between entities. However, for dynamic real-world applications such as social networks, recommender systems, computational biology, relational knowledge representation has emerged as a challenging research problem where there is a need to represent the changing nodes, attributes, and edges over time. The evolution of search engine responses to user queries in the last few years is partly because of the role of KGs such as Google KG. KGs are significantly contributing to various AI applications from link prediction, entity relations prediction, node classification to recommendation and question answering systems. This article is an attempt to summarize the journey of KG for AI.

Researchers at the San Diego Supercomputer Center at UC San Diego and UC San Diego School of Medicine have received two National Science Foundation (NSF) planning grants worth a combined $2 million under a new NSF initiative to invest in research collaborations between academia, industry, government and communities that enable capabilities beyond what is currently possible in either the private or public sectors. Called Convergence Accelerator awards, the first set of grants has been awarded to research teams, according to a recent NSF release. These projects will evaluate how employers can use sophisticated artificial intelligence tools to connect with the workers they need, while seeking ways to develop the future U.S. workforce with the universities that will educate people and the companies that will employ them. A total of 43 new awards totaling $39 million will support projects across the country. Both grants, which support one of NSF's'Big Ideas' called Harnessing the Data Revolution, are focused on the area of Open Knowledge Networks, which pool many types of information and ideas so they can be accessed and leveraged to create new understanding.

D. Tyler McQuade, Ph.D, professor in the Department of Chemical and Life Science Engineering at Virginia Commonwealth University College of Engineering, is principal investigator of a multi-university project seeking to use artificial intelligence to help scientists come up with the perfect molecule for everything from a better shampoo to coatings on advanced microchips. The project is one of the first in the U.S. to be selected for $994,433 in funding as part of a new pilot project of the National Science Foundation (NSF) called the Convergence Accelerator (C-Accel). McQuade and his collaborators will pitch their prototype in March 2020 in a bid for additional funding of up to $5 million over five years. Adam Luxon, a Ph.D. student in the Department of Chemical and Life Science Engineering who has been involved from the beginning, explained it this way: "We want to essentially make the Alexa of chemistry." Just as Amazon, Google and Netflix use data algorithms to suggest customized predictions, the team plans to build a platform and open knowledge network that can combine and help users make sense of molecular sciences data pulled from a wide range of sources including academia, industry and government.