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

TL;DR

This paper emphasizes the importance of integrating knowledge into machine learning to improve understanding of complex, multimodal content, especially when data is scarce or objects are highly subjective.

Contribution

It highlights the critical role of knowledge creation and utilization in advancing machine understanding, proposing a focus on multimodal data and knowledge integration.

Findings

Knowledge enhances understanding of complex content

Integration of knowledge can compensate for limited data

Progress in multimodal understanding is imminent

Abstract

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.