The output of large language models (LLMs) is unstable, due both to non-determinism of the decoding process as well as to prompt brittleness. While the intrinsic non-determinism of LLM generation may mimic existing uncertainty in human annotations through distributional shifts in outputs, it is largely assumed, yet unexplored, that the prompt brittleness effect is unique to LLMs. This raises the question: do human annotators show similar sensitivity to prompt changes? If so, should prompt brittleness in LLMs be considered problematic? One may alternatively hypothesize that prompt brittleness correctly reflects human annotation variances. To fill this research gap, we systematically compare the effects of prompt modifications on LLMs and identical instruction modifications for human annotators, focusing on the question of whether humans are similarly sensitive to prompt perturbations. To study this, we prompt both humans and LLMs for a set of text classification tasks conditioned on prompt variations. Our findings indicate that both humans and LLMs exhibit increased brittleness in response to specific types of prompt modifications, particularly those involving the substitution of alternative label sets or label formats. However, the distribution of human judgments is less affected by typographical errors and reversed label order than that of LLMs.

New York's new mayor Zohran Mamdani tells Trump'I have four words for you' in blistering victory speech quoting his socialist hero, bragging about'toppling a dynasty' and promising a'new dawn' Driver screaming'Allahu Akbar' ploughs in to pedestrians'trying to hit everyone he encountered' on French holiday island leaving ten injured This Leftist election landslide was caused by the same vile disease that's triggered a GOP civil war. Why Mamdani's socialist revolution in New York has sparked a civil war for Democrats... and Trump is secretly loving it Simone Biles details all the plastic surgery she's had after her boob job this summer Kim Kardashian's new TV show All's Fair is SAVAGED by critics as it's branded'the worst drama ever', 'existentially terrible' and'a crime against television' while debuting at 0% on Rotten Tomatoes Putin orders increased drone'incursions' in Europe as Russia ramps up'hybrid war' - with Belgium's biggest airport forced to close overnight Inside Kate and William's forever home: Princess is kitting out Forest Lodge in her preferred'classic contemporary style' to create a'lovely but absolutely inoffensive' look REVEALED: Fattest states in America ranked... including region where three-quarters of residents are obese I was so desperate for a baby I stole sperm from my husband's condom: It's the most shocking confession. Now for the first time LIZ JONES tells what happened next... and the consequence no one saw New footage reveals the moments before football manager collapsed and died mid-match, leaving his players in disbelief, as it emerges he'complained about fish he had eaten' hours before Hollywood A-listers may be blacklisted for'antisemitism' under Paramount's new anti-woke leadership Texas teen'tears masterpiece from wall at the Met in unhinged meltdown' before being handed in by his MOTHER Bizarre TikTok trend sees Gen Z'rawdogging boredom' to fix their attention spans - so, does it really work? READ MORE: Mark Zuckerberg's'rawdog' routine that made him a billionaire A bizarre new trend has emerged on TikTok, in which Gen Z put themselves in timeout to try to fix their attention spans. Dubbed'rawdogging boredom', users set a timer and simply sit there without any distractions.

There is a general consensus that body movements and postures provide important cues for idennullfying emonullonal states, parnullcularly when facial and vocal signals are unavailable [1]. Emonullonal Body Language (EBL) is rapidly emerging as a significant area of research within cogninullve and affecnullve neuroscience. According to De Gelder [10], numerous valuable insights into human emonullon and its neurobiological foundanullons have been derived from the study of facial expressions. Indeed certain emonullons are more effecnullvely conveyed through facial expressions, while others are benuller commun icated through body movements or a combinanullon of both. Gestures provide observable cues that can be instrumental in recognizing and interprenullng a user's emonullonal state, especially in the absence of verbal or facial signals.

Sign up for the Slatest to get the most insightful analysis, criticism, and advice out there, delivered to your inbox daily. I once witnessed a friend going through a severe midlife crisis. Basically overnight, this formerly serious and well-adjusted middle-aged man dumped his wife for a much younger girlfriend, got a face tattoo, and built a full-sized halfpipe in his house. Soon, we were barraged with music recommendations (all stuff he'd listened to in high school and college) and life updates laden with "hip" "slang" ("Despite the age gap, my situationship with Triniteigh is lowkey lit"). It was a transparent--and, from a certain perspective, even sympathetic--response to a universal anxiety: He'd seen that the good times were over, and that only decline lay ahead. But, like all nostalgists, he didn't realize that you can't ever truly go back; you can only go backward. The United States, under President Donald Trump, seems to be undergoing a similar midlife crisis, as this reactionary administration attempts to brute-force the country back to a golden age that many people are realizing either didn't exist in the first place or has been permanently lost to the mists of time and modernization.

In recent times, online education and the usage of video-conferencing platforms have experienced massive growth. Due to the limited scope of a virtual classroom, it may become difficult for instructors to analyze learners' attention and comprehension in real time while teaching. In the digital mode of education, it would be beneficial for instructors to have an automated feedback mechanism to be informed regarding learners' attentiveness at any given time. This research presents a novel computer vision-based approach to analyze and quantify learners' attentiveness, engagement, and other affective states within online learning scenarios. This work presents the development of a multiclass multioutput classification method using convolutional neural networks on a publicly available dataset - DAiSEE. A machine learning-based algorithm is developed on top of the classification model that outputs a comprehensive attentiveness index of the learners. Furthermore, an end-to-end pipeline is proposed through which learners' live video feed is processed, providing detailed attentiveness analytics of the learners to the instructors. By comparing the experimental outcomes of the proposed method against those of previous methods, it is demonstrated that the proposed method exhibits better attentiveness detection than state-of-the-art methods. The proposed system is a comprehensive, practical, and real-time solution that is deployable and easy to use. The experimental results also demonstrate the system's efficiency in gauging learners' attentiveness.

Background: Recent advancements in Artificial Intelligence (AI) contributed significantly to suicide assessment, however, our theoretical understanding of this complex behavior is still limited. Objective: This study aimed to harness AI methodologies to uncover hidden risk factors that trigger or aggravate suicide behaviors. Method: The primary dataset included 228,052 Facebook postings by 1,006 users who completed the gold-standard Columbia Suicide Severity Rating Scale. This dataset was analyzed using a bottom-up research pipeline without a-priory hypotheses and its findings were validated using a top-down analysis of a new dataset. This secondary dataset included responses by 1,062 participants to the same suicide scale as well as to well-validated scales measuring depression and boredom. Results: An almost fully automated, AI-guided research pipeline resulted in four Facebook topics that predicted the risk of suicide, of which the strongest predictor was boredom. A comprehensive literature review using APA PsycInfo revealed that boredom is rarely perceived as a unique risk factor of suicide. A complementing top-down path analysis of the secondary dataset uncovered an indirect relationship between boredom and suicide, which was mediated by depression. An equivalent mediated relationship was observed in the primary Facebook dataset as well. However, here, a direct relationship between boredom and suicide risk was also observed. Conclusions: Integrating AI methods allowed the discovery of an under-researched risk factor of suicide. The study signals boredom as a maladaptive 'ingredient' that might trigger suicide behaviors, regardless of depression. Further studies are recommended to direct clinicians' attention to this burdening, and sometimes existential experience.

The provision of robotic assistance during motor training has proven to be effective in enhancing motor learning in some healthy trainee groups as well as patients. Personalizing such robotic assistance can help further improve motor (re)learning outcomes and cater better to the trainee's needs and desires. However, the development of personalized haptic assistance is hindered by the lack of understanding of the link between the trainee's personality and the effects of haptic guidance during human-robot interaction. To address this gap, we ran an experiment with 42 healthy participants who trained with a robotic device to control a virtual pendulum to hit incoming targets either with or without haptic guidance. We found that certain personal traits affected how users adapt and interact with the guidance during training. In particular, those participants with an 'Achiever gaming style' performed better and applied lower interaction forces to the robotic device than the average participant as the training progressed. Conversely, participants with the 'Free spirit game style' increased the interaction force in the course of training. We also found an interaction between some personal characteristics and haptic guidance. Specifically, participants with a higher 'Transformation of challenge' trait exhibited poorer performance during training while receiving haptic guidance compared to an average participant receiving haptic guidance. Furthermore, individuals with an external Locus of Control tended to increase their interaction force with the device, deviating from the pattern observed in an average participant under the same guidance. These findings suggest that individual characteristics may play a crucial role in the effectiveness of haptic guidance training strategies.

Fluent human-robot collaboration requires a robot teammate to understand, learn, and adapt to the human's psycho-physiological state. Such collaborations require a computing system that monitors human physiological signals during human-robot collaboration (HRC) to quantitatively estimate a human's level of comfort, which we have termed in this research as comfortability index (CI) and uncomfortability index (unCI). Subjective metrics (surprise, anxiety, boredom, calmness, and comfortability) and physiological signals were collected during a human-robot collaboration experiment that varied robot behavior. The emotion circumplex model is adapted to calculate the CI from the participant's quantitative data as well as physiological data. To estimate CI/unCI from physiological signals, time features were extracted from electrocardiogram (ECG), galvanic skin response (GSR), and pupillometry signals. In this research, we successfully adapt the circumplex model to find the location (axis) of 'comfortability' and 'uncomfortability' on the circumplex model, and its location match with the closest emotions on the circumplex model. Finally, the study showed that the proposed approach can estimate human comfortability/uncomfortability from physiological signals.

Recent research has employed reinforcement learning (RL) algorithms to optimize long-term user engagement in recommender systems, thereby avoiding common pitfalls such as user boredom and filter bubbles. They capture the sequential and interactive nature of recommendations, and thus offer a principled way to deal with long-term rewards and avoid myopic behaviors. However, RL approaches are intractable in the slate recommendation scenario - where a list of items is recommended at each interaction turn - due to the combinatorial action space. In that setting, an action corresponds to a slate that may contain any combination of items. While previous work has proposed well-chosen decompositions of actions so as to ensure tractability, these rely on restrictive and sometimes unrealistic assumptions. Instead, in this work we propose to encode slates in a continuous, low-dimensional latent space learned by a variational auto-encoder. Then, the RL agent selects continuous actions in this latent space, which are ultimately decoded into the corresponding slates. By doing so, we are able to (i) relax assumptions required by previous work, and (ii) improve the quality of the action selection by modeling full slates instead of independent items, in particular by enabling diversity. Our experiments performed on a wide array of simulated environments confirm the effectiveness of our generative modeling of slates over baselines in practical scenarios where the restrictive assumptions underlying the baselines are lifted. Our findings suggest that representation learning using generative models is a promising direction towards generalizable RL-based slate recommendation.

IN 1992, THE poet Anne Carson published a little book called Short Talks. It's a series of micro-essays, ranging in length from a sentence to a paragraph, on seemingly disconnected subjects--orchids, rain, the mythic Andean vicuña. Her "Short Talk on the Sensation of Airplane Takeoff" is what it sounds like. Her "Short Talk on Trout" is mostly about the types of trout that appear in haiku. In what passes for the book's introduction, Carson writes, with dry Canadian relatability, "I will do anything to avoid boredom. It is the task of a lifetime."