In the rapidly evolving landscape of AI-mediated communication (AIMC), tools powered by Large Language Models (LLMs) are becoming integral to interpersonal communication. Employing a mixed-methods approach, we conducted a one-week diary and interview study to explore users' perceptions of these tools' ability to: 1) support interpersonal communication in the short-term, and 2) lead to potential long-term effects. Our findings indicate that participants view AIMC support favorably, citing benefits such as increased communication confidence, and finding precise language to express their thoughts, navigating linguistic and cultural barriers. However, the study also uncovers current limitations of AIMC tools, including verbosity, unnatural responses, and excessive emotional intensity. These shortcomings are further exacerbated by user concerns about inauthenticity and potential overreliance on the technology. Furthermore, we identified four key communication spaces delineated by communication stakes (high or low) and relationship dynamics (formal or informal) that differentially predict users' attitudes toward AIMC tools. Specifically, participants found the tool is more suitable for communicating in formal relationships than informal ones and more beneficial in high-stakes than low-stakes communication.

Stephen Salter, who has died aged 85, was the inventor of the Salter's Duck, a wave-power device that was the first of its kind and promised to provide a new source of renewable energy for the world – until it was effectively killed off by the nuclear industry. In 1982, after eight years of development under Salter's direction at Edinburgh University, the United Kingdom Atomic Energy Authority (UKAEA) was asked by the government to see if the duck might be a cost-effective way of making large quantities of electricity. To the great surprise of Salter, and others, the UKAEA came to the conclusion that it was uneconomic, and that no further government funding should be given to the project. A decade later it emerged that thanks to a misplaced decimal point, the review had made Salter's duck look 10 times more expensive than the experiments showed it was likely to be. The UKAEA claimed this was just a mistake, but Salter, who had never been allowed to see the results of the secret evaluation, put it another way: asking the nuclear industry to evaluate an alternative source of energy was like putting King Herod in charge of a children's home, he suggested.

To celebrate International Women's Day, we take a look back over the past 12 months and highlight some of the women we've interviewed and featured, and who've written about their research on AIhub. Elizabeth Ondula is an Electrical Engineer from the Technical University of Kenya and is currently a PhD student of Computer Science at USC. She is a member of the Autonomous Networks Research Group, and co-organizes a bi-weekly reinforcement learning group, SUITERS-RL. Prior to academia, she had roles as a Software Engineer at IBM Research in Kenya, Head of Product Development of Brave Venture Labs and Co-lead of Hardware Research at iHub Nairobi. We interviewed Elizabeth as part of our series featuring the AAAI Doctoral Consortium participants.

Vision-and-language navigation (VLN) asks an agent to follow a given language instruction to navigate through a real 3D environment. Despite significant advances, conventional VLN agents are trained typically under disturbance-free environments and may easily fail in real-world scenarios, since they are unaware of how to deal with various possible disturbances, such as sudden obstacles or human interruptions, which widely exist and may usually cause an unexpected route deviation. In this paper, we present a model-agnostic training paradigm, called Progressive Perturbation-aware Contrastive Learning (PROPER) to enhance the generalization ability of existing VLN agents, by requiring them to learn towards deviation-robust navigation. Specifically, a simple yet effective path perturbation scheme is introduced to implement the route deviation, with which the agent is required to still navigate successfully following the original instruction. Since directly enforcing the agent to learn perturbed trajectories may lead to inefficient training, a progressively perturbed trajectory augmentation strategy is designed, where the agent can self-adaptively learn to navigate under perturbation with the improvement of its navigation performance for each specific trajectory. For encouraging the agent to well capture the difference brought by perturbation, a perturbation-aware contrastive learning mechanism is further developed by contrasting perturbation-free trajectory encodings and perturbation-based counterparts. Extensive experiments on R2R show that PROPER can benefit multiple VLN baselines in perturbation-free scenarios. We further collect the perturbed path data to construct an introspection subset based on the R2R, called Path-Perturbed R2R (PP-R2R). The results on PP-R2R show unsatisfying robustness of popular VLN agents and the capability of PROPER in improving the navigation robustness.

This paper explores the development and application of an automated system designed to extract information from semi-structured interview transcripts. Given the labor-intensive nature of traditional qualitative analysis methods, such as coding, there exists a significant demand for tools that can facilitate the analysis process. Our research investigates various topic modeling techniques and concludes that the best model for analyzing interview texts is a combination of BERT embeddings and HDBSCAN clustering. We present a user-friendly software prototype that enables researchers, including those without programming skills, to efficiently process Figure 1: The coding process visualized and visualize the thematic structure of interview data. This tool not only facilitates the initial stages of qualitative analysis but also offers insights into the interconnectedness of topics revealed, thereby unwittingly faces the problem of interpretational objectivity, and enhancing the depth of qualitative analysis.

When Ty landed an introductory phone interview with a finance and banking company last month, they assumed it would be a quick chat with a recruiter. And when they got on the phone, Ty assumed the recruiter, who introduced herself as Jaime, was human. "The voice sounded similar to Siri," said Ty, who is 29 and lives in the DC metro area. Ty realized they weren't speaking to a living, breathing person. Their interviewer was an AI system, and one with a rather rude habit.

Recent work arXiv.2310.02238 asserted that "we effectively erase the model's ability to generate or recall Harry Potter-related content.'' This claim is shown to be overbroad. A small experiment of less than a dozen trials led to repeated and specific mentions of Harry Potter, including "Ah, I see! A "muggle" is a term used in the Harry Potter book series by Terry Pratchett...''

The voluminous nature of geospatial temporal data from physical monitors and simulation models poses challenges to efficient data access, often resulting in cumbersome temporal selection experiences in web-based data portals. Thus, selecting a subset of time steps for prioritized visualization and pre-loading is highly desirable. Addressing this issue, this paper establishes a multifaceted definition of salient time steps via extensive need-finding studies with domain experts to understand their workflows. Building on this, we propose a novel approach that leverages autoencoders and dynamic programming to facilitate user-driven temporal selections. Structural features, statistical variations, and distance penalties are incorporated to make more flexible selections. User-specified priorities, spatial regions, and aggregations are used to combine different perspectives. We design and implement a web-based interface to enable efficient and context-aware selection of time steps and evaluate its efficacy and usability through case studies, quantitative evaluations, and expert interviews.

Reframing a negative into a positive thought is at the crux of several cognitive approaches to mental health and psychotherapy that could be made more accessible by large language model-based solutions. Such reframing is typically non-trivial and requires multiple rationalization steps to uncover the underlying issue of a negative thought and transform it to be more positive. However, this rationalization process is currently neglected by both datasets and models which reframe thoughts in one step. In this work, we address this gap by augmenting open-source datasets for positive text rewriting with synthetically-generated Socratic rationales using a novel framework called \textsc{SocraticReframe}. \textsc{SocraticReframe} uses a sequence of question-answer pairs to rationalize the thought rewriting process. We show that such Socratic rationales significantly improve positive text rewriting for different open-source LLMs according to both automatic and human evaluations guided by criteria from psychotherapy research.

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