Structured knowledge is important for many AI applications. Commonsense knowledge, which is crucial for robust human-centric AI, is covered by a small number of structured knowledge projects. However, they lack knowledge about human traits and behaviors conditioned on socio-cultural contexts, which is crucial for situative AI. This paper presents Candle, an end-to-end methodology for extracting high-quality cultural commonsense knowledge (CCSK) at scale. Candle extracts CCSK assertions from a huge web corpus and organizes them into coherent clusters, for 3 domains of subjects (geography, religion, occupation) and several cultural facets (food, drinks, clothing, traditions, rituals, behaviors). Candle includes judicious techniques for classification-based filtering and scoring of interestingness. Experimental evaluations show the superiority of the Candle CCSK collection over prior works, and an extrinsic use case demonstrates the benefits of CCSK for the GPT-3 language model. Code and data can be accessed at https://candle.mpi-inf.mpg.de/.

Recent advances in AI include a Law firm hiring a robot lawyer and companies developing autonomous vehicles with robot drivers. Findings from our study have gauged the current cognitive capacity of such systems, indicating areas for improvement. We focus on autonomous vehicles, i.e., those that conduct automated driving and need to make autonomous, i.e., independent decisions. We propose an approach enabled with commonsense knowledge (CSK) from worldwide repositories to simulate intuitive humanlike decision-making in autonomous vehicles. We consider the repository WebChild with a multitude of CSK concepts, properties and relations. We investigate this and related domain-specific knowledge bases (domain KBs) to harness them within our proposed approach. Accordingly we build a transportation domain KB incorporating CSK and the needs of autonomous vehicles. This would be useful in guiding automated driving and making the systems get closer to the thresholds of human cognition. This work thereby makes contributions to smart mobility in smart cities. The paper presents our vision with design, implementation, experiments, recommendations and a future roadmap. As a broader impact, it propels more joint work between AI, Law and related areas.

In computational analysis in scientific domains, images are often compared based on their features, e.g., size, depth and other domain-specific aspects. Certain features may be more significant than others while comparing the images and drawing corresponding inferences for specific applications. Though domain experts may have subjective notions of similarity for comparison, they seldom have a distance function that ranks the image features based on their relative importance. We propose a method called features rank for learning such a distance function in order to capture the semantics of the images. We are given training samples with pairs of images and the extent of similarity identified for each pair. Using a guessed initial distance function. Features rank clusters the given images in levels. It then adjusts the distance junction based on the error between the clusters and training samples using heuristics proposed in this paper. The distance junction that gives the lowest error is the output. This contains the features ranked in the order most appropriate the domain. Features rank is evaluated with real image data from nanotechnology and bioinformatics. The results of our evaluation are presented in the paper.