This work addresses the challenge of capturing the complexities of legal knowledge by proposing a multi-layered embedding-based retrieval method for legal and legislative texts. Creating embeddings not only for individual articles but also for their components (paragraphs, clauses) and structural groupings (books, titles, chapters, etc), we seek to capture the subtleties of legal information through the use of dense vectors of embeddings, representing it at varying levels of granularity. Our method meets various information needs by allowing the Retrieval Augmented Generation system to provide accurate responses, whether for specific segments or entire sections, tailored to the user's query. We explore the concepts of aboutness, semantic chunking, and inherent hierarchy within legal texts, arguing that this method enhances the legal information retrieval. Despite the focus being on Brazil's legislative methods and the Brazilian Constitution, which follow a civil law tradition, our findings should in principle be applicable across different legal systems, including those adhering to common law traditions. Furthermore, the principles of the proposed method extend beyond the legal domain, offering valuable insights for organizing and retrieving information in any field characterized by information encoded in hierarchical text.

Modeling place functions from a computational perspective is a prevalent research topic. The technology of embedding enables a new approach that allows modeling the function of a place by its chronological context as part of a trajectory. The embedding similarity was previously proposed as a new metric for measuring the similarity of place functions, with some preliminary results. This study explores if this approach is meaningful for geographical units at a much smaller geographical granularity compared to previous studies. In addition, this study investigates if the geographical distance can influence the embedding similarity. The empirical evaluations based on a big vehicle trajectory data set confirm that the embedding similarity can be a metric proxy for place functions. However, the results also show that the embedding similarity is still bounded by the distance at the local scale.

Scientists in multiple fields of psychology are actively gathering data and undergoing testing in an effort to teach artificial intelligence programs to diagnose mental illness in humans. This is according to a report in The Verge written by B. David Zarley, who himself has borderline personality disorder, as part of its Real World AI issue. Zarley met with multiple scientists who are each taking their own approach to machine learning in the service of finding a better way to diagnose psychological disorders. Sponsored adThis sponsor paid to have this advertisement placed in this section. The current model, based on referring to the DSM to guide psychiatrists to make diagnoses around a patient's self-reported symptoms, is inherently biased and considered by many in the field of psychology to be flawed.

In a symposium focused on deception and counter-deception in machines, one might be immediately drawn to a narrow conception of those phenomena which highlight the pernicious ways in which they might be used. On the broader notion of fibbing that we describe in our talk, the social function of being fast and loose with the truth takes center stage as a tool for accomplishing a wide variety of socially centered goals. We briefly review the FIDE framework, described in (Isaac & Bridewell 2014; Bridewell & Bello 2014), including the conceptual resources it requires and the variety of fib-related concepts it supports. FIDE delineates between the aforementioned concepts as ends, and the strategic means by which the fibber might achieve these ends. In doing so, we show that certain types of difficult to conceptualize behavior, most notably bullshitting (Frankfurt 2006) and responses to bullshitting, are instances of a kind of strategy for impression management that serves higher-order social goals.