Using domain knowledge to improve Artificial Intelligence
One of the challenges in applying artificial intelligence, and deep learning in particular, is a lack of representative data and/or labels. In some extreme cases, no labeled data samples are available at all. Besides the problem of data, applied models often have a lack of understanding the application’s context. Our research focuses on bridging that gap, such that deep learning can be applied with few labels, and that learned models have more context awareness.
In this talk, I will show why this is important, and some approaches that may solve some of these issues. A key topic is to leverage domain or expert knowledge in machine learning. This offers a step towards more robust and aware applications of artificial intelligence.