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Machine Learning has been a big success story during the AI resurgence. Oneparticular stand out success relates to learning from a massive amount of data.In spite of early assertions of the unreasonable effectiveness of data, thereis increasing recognition for utilizing knowledge whenever it is available orcan be created purposefully. In this paper, we discuss the indispensable roleof knowledge for deeper understanding of content where (i) large amounts oftraining data are unavailable, (ii) the objects to be recognized are complex,(e.g., implicit entities and highly subjective content), and (iii) applicationsneed to use complementary or related data in multiple modalities/media. Whatbrings us to the cusp of rapid progress is our ability to (a) create relevantand reliable knowledge and (b) carefully exploit knowledge to enhance ML/NLPtechniques. Using diverse examples, we seek to foretell unprecedented progressin our ability for deeper understanding and exploitation of multimodal data andcontinued incorporation of knowledge in learning techniques.

Knowledge will Propel Machine Understanding of Content: Extrapolating from Current Examples