Building and maintaining High-Definition (HD) maps represents a large barrier to autonomous vehicle deployment. This, along with advances in modern online map detection models, has sparked renewed interest in the online mapping problem. However, effectively predicting online maps at a high enough quality to enable safe, driverless deployments remains a significant challenge. Recent work on these models proposes training robust online mapping systems using low quality map priors with synthetic perturbations in an attempt to simulate out-of-date HD map priors. In this paper, we investigate how models trained on these synthetically perturbed map priors generalize to performance on deployment-scale, real world map changes. We present a large-scale experimental study to determine which synthetic perturbations are most useful in generalizing to real world HD map changes, evaluated using multiple years of real-world autonomous driving data. We show there is still a substantial sim2real gap between synthetic prior perturbations and observed real-world changes, which limits the utility of current prior-informed HD map prediction models.

Knowledge Bases (KBs) provide structured representation of the real-world in the form of extensive collections of facts about real-world entities, their properties and relationships. They are ubiquitous in large-scale intelligent systems that exploit structured information such as in tasks like structured search, question answering and reasoning, and hence their data quality becomes paramount. The inevitability of change in the real-world, brings us to a central property of KBs -- they are highly dynamic in that the information they contain are constantly subject to change. In other words, KBs are unstable. In this paper, we investigate the notion of KB stability, specifically, the problem of KBs changing due to real-world change. Some entity-property-pairs do not undergo change in reality anymore (e.g., Einstein-children or Tesla-founders), while others might well change in the future (e.g., Tesla-board member or Ronaldo-occupation as of 2022). This notion of real-world grounded change is different from other changes that affect the data only, notably correction and delayed insertion, which have received attention in data cleaning, vandalism detection, and completeness estimation already. To analyze KB stability, we proceed in three steps. (1) We present heuristics to delineate changes due to world evolution from delayed completions and corrections, and use these to study the real-world evolution behaviour of diverse Wikidata domains, finding a high skew in terms of properties. (2) We evaluate heuristics to identify entities and properties likely to not change due to real-world change, and filter inherently stable entities and properties. (3) We evaluate the possibility of predicting stability post-hoc, specifically predicting change in a property of an entity, finding that this is possible with up to 83% F1 score, on a balanced binary stability prediction task.

Correctly labelling training data for AI models is vital to avoid serious problems, as is using sufficiently large datasets. However, manually labelling massive amounts of data is time-consuming and laborious. Using pre-labelled datasets can be problematic, as evidenced by MIT having to pull its 80 Million Tiny Images datasets. For those unaware, the popular dataset was found to contain thousands of racist and misogynistic labels that could have been used to train AI models. AI News caught up with Devang Sachdev, VP of Marketing at Snorkel AI, to find out how the company is easing the laborious process of labelling data in a safe and effective way. AI News: How is Snorkel helping to ease the laborious process of labelling data?