We present a novel formalisation of tensor semantics for linear temporal logic on finite traces (LTLf), with formal proofs of correctness carried out in the theorem prover Isabelle/HOL. We demonstrate that this formalisation can be integrated into a neurosymbolic learning process by defining and verifying a differentiable loss function for the LTLf constraints, and automatically generating an implementation that integrates with PyTorch. We show that, by using this loss, the process learns to satisfy pre-specified logical constraints. Our approach offers a fully rigorous framework for constrained training, eliminating many of the inherent risks of ad-hoc, manual implementations of logical aspects directly in an "unsafe" programming language such as Python, while retaining efficiency in implementation.

Interpersonal trust - a shared display of confidence and vulnerability toward other individuals - can be seen as instrumental in the development of human societies. Safra, Chevallier, Gr\`ezes, and Baumard (2020) studied the historical progression of interpersonal trust by training a machine learning (ML) algorithm to generate trustworthiness ratings of historical portraits, based on facial features. They reported that trustworthiness ratings of portraits dated between 1500--2000CE increased with time, claiming that this evidenced a broader increase in interpersonal trust coinciding with several metrics of societal progress. We argue that these claims are confounded by several methodological and analytical issues and highlight troubling parallels between Safra et al.'s algorithm and the pseudoscience of physiognomy. We discuss the implications and potential real-world consequences of these issues in further detail.

If the recent news from Facebook's F8 developer conference is any indication, the future of online communication between businesses and consumers will occur more often via AI-driven chatbots. Chatbots are a staple in WeChat and have popped up across social media messaging channels, like on Twitter and LinkedIn. While chatbots look to improve customer communication experiences in-app, online and in messaging services, they're only half of the equation, said Frank Chevallier, VP of Software Products at LiveWorld. LiveWorld has been in the business of facilitating communication since the days of dial-up. The company broke into facilitating online communication between brands and customers back in the days when those conversations took place on forums, Chevallier said.