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