We describe how we built a very large probabilistic database of declarative facts, called "Knowledge Vault", by applying "machine reading" to the web. This approach extends previous work, such as NELL and YAGO, by leveraging existing knowledge bases as a form of "prior". We also discuss our new nascent efforts to extract procedural knowledge from videos on the web. This requires training visual detectors from weakly labeled data. We give an example where we attempt to interpret cooking videos by aligning the frames to the steps of a recipe.