Knowledge will Propel Machine Understanding of Content: Extrapolating from Current Examples
This work highlights a key problem for AI researchers and practitioners in improving machine understanding under data-scarce or complex scenarios, but it is incremental as it builds on existing recognition of knowledge's importance.
The paper addresses the challenge of achieving deeper content understanding in machine learning when large datasets are unavailable, objects are complex, or multimodal data is involved, by emphasizing the role of knowledge to enhance ML/NLP techniques. It predicts rapid progress in multimodal data understanding through the creation and exploitation of knowledge.
Machine Learning has been a big success story during the AI resurgence. One particular stand out success relates to learning from a massive amount of data. In spite of early assertions of the unreasonable effectiveness of data, there is increasing recognition for utilizing knowledge whenever it is available or can be created purposefully. In this paper, we discuss the indispensable role of knowledge for deeper understanding of content where (i) large amounts of training data are unavailable, (ii) the objects to be recognized are complex, (e.g., 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 relevant and reliable knowledge and (b) carefully exploit knowledge to enhance ML/NLP techniques. Using diverse examples, we seek to foretell unprecedented progress in our ability for deeper understanding and exploitation of multimodal data and continued incorporation of knowledge in learning techniques.