  Machine Learning has been a big success story during the AI resurgence. One
particular stand out success relates to unsupervised learning from a massive
amount of data, albeit much of it relates to one modality/type of data at a
time. In spite of early assertions of the unreasonable effectiveness of data,
there is increasing recognition of utilizing knowledge whenever it is available
or can be created purposefully. In this paper, we focus on discussing the
indispensable role of knowledge for deeper understanding of complex text and
multimodal data in situations where (i) large amounts of training data
(labeled/unlabeled) are not available or labor intensive to create, (ii) the
objects (particularly text) to be recognized are complex (i.e., beyond simple
entity-person/location/organization names), such as implicit entities and
highly subjective content, and (iii) applications need to use complementary or
related data in multiple modalities/media. What brings us to the cusp of rapid
progress is our ability to (a) create knowledge, varying from comprehensive or
cross domain to domain or application specific, and (b) carefully exploit the
knowledge to further empower or extend the applications of ML/NLP techniques.
Using the early results in several diverse situations - both in data types and
applications - we seek to foretell unprecedented progress in our ability for
deeper understanding and exploitation of multimodal data.
