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Grant Helps Shape AI Future

A couple of years ago, Salesforce launched its own AI platform called Einstein. It enabled companies to deliver predictive, personalized, and smarter customer experiences. Since then we’ve included more than 30 AI features in our CRM.

To further develop Einstein’s AI feature, Salesforce announced the awarding of research grants for non-profit organizations, and university faculty and researches. The program’s goal is to recognize individuals that will help share AI’s future through their innovative ideas. Salesforce will provide funds up to $50,000, depending on the support needs of the research.

Application Process

Applicants to the grant were required to submit a 2-page document in PDF formation which contains details on the proposed research and its impact on the research community

Candidates were encouraged to recognize what the research would enable and the projected outcome if the vision is achieved. Proposed test cases should be detailed together with the expected challenges and mitigation plans.

The budget overview should also be included in the proposal detailing how the funding will be applied. Additional funding for the research should also be mentioned. Applicants are also recommended to discuss their previous citations and research papers if any.

Grant Winners

We were amazed by the variety and quality of the proposals that were submitted. The five winners of the grants are:

Tianfu Wu of North Carolina State University

Computer Vision & Natural Language Processing

Learning Deep Grammar Networks for Visual Question Answering

Mohit Bansal of University of North Carolina, Chapel Hill

Natural Language Processing

Multi-Task Multimodal Translation and Content Selection

Junyi Jessy Li & Katrin Erk of University of Texas at Austin

Natural Language Processing

Hierarchical Graph-based Advice Summarization from Online Forums

Quanquan Gu of University of CA, Los Angeles

Machine Learning

Understanding and Advancing Nonconvex Optimization for Deep Learning

Zachary Chase Lipton of Carnegie Mellon University

Data Mining

Failing Loudly: Detecting, Quantifying, and Interpreting Distribution Shift

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