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Emotion-aware customer service needs in-domain conversational data, rich annotations, and predictive capabilities, but existing resources for emotion recognition are often out-of-domain, narrowly labeled, and focused on post-hoc detection. To address this, we conducted a controlled Wizard of Oz (WOZ) experiment to elicit interactions with targeted affective trajectories. The resulting corpus, EmoWOZ-CS, contains 2,148 bilingual (Dutch-English) written dialogues from 179 participants across commercial aviation, e-commerce, online travel agencies, and telecommunication scenarios. Our contributions are threefold: (1) Evaluate WOZ-based operator-steered valence trajectories as a design for emotion research; (2) Quantify human annotation performance and variation, including divergences between self-reports and third-party judgments; (3) Benchmark detection and forward-looking emotion inference in real-time support. Findings show neutral dominates participant messages; desire and gratitude are the most frequent non-neutral emotions. Agreement is moderate for multilabel emotions and valence, lower for arousal and dominance; self-reports diverge notably from third-party labels, aligning most for neutral, gratitude, and anger. Objective strategies often elicit neutrality or gratitude, while suboptimal strategies increase anger, annoyance, disappointment, desire, and confusion. Some affective strategies (cheerfulness, gratitude) foster positive reciprocity, whereas others (apology, empathy) can also leave desire, anger, or annoyance. Temporal analysis confirms successful conversation-level steering toward prescribed trajectories, most distinctly for negative targets; positive and neutral targets yield similar final valence distributions. Benchmarks highlight the difficulty of forward-looking emotion inference from prior turns, underscoring the complexity of proactive emotion-aware support.

Sometimes journalists befuddle me, and I'm a journalist – although my touchy detractors would dispute that. Perhaps like you, I have been watching – with a healthy dose of bemusement and amusement – the outrage-du-jour dominate the latest 24-hour "news cycle" in North America and beyond. Such is the squirrel-like attention span of many of my perpetually outraged colleagues, that today's outrage usually has a short life expectancy since another outrage inevitably comes along tomorrow. But the outrage seizing Washington, DC – the capital of outrage – appears poised to consume the Beltway press corps for more than a day or two. When that happens, the outrage tends to evolve into a four-alarm scandal which journalists crave because it often translates into a big, ego-boosting award for the lucky scribe who triggered the original outrage.

In a surprise move, OpenAI's chief technology officer announced on Wednesday that she would soon leave the company after six and a half years. In a note shared with the company and then posted to Twitter/X, Mira Murati wrote she was leaving the tech company behind ChatGPT. "After much reflection, I have made the difficult decision to leave OpenAI … I'm stepping away because I want to create the time and space to do my own exploration," she said. CEO Sam Altman offered kind words in response to Murati's departure, writing on X: "I feel tremendous gratitude towards her for what she has helped us build and accomplish, but I most of all feel personal gratitude towards her for the support and love during all the hard times. I am excited for what she'll do next."

Hi all, I have something to share with you. After much reflection, I have made the difficult decision to leave OpenAl. My six-and-a-half years with the OpenAl team have been an extraordinary privilege. While I'll express my gratitude to many individuals in the coming days, I want to start by thanking Sam and Greg for their trust in me to lead the technical organization and for their support throughout the years. There's never an ideal time to step away from a place one cherishes, yet this moment feels right.

While small businesses are increasingly turning to online crowdfunding platforms for essential funding, over 40% of these campaigns may fail to raise any money, especially those from low socio-economic areas. We utilize the latest advancements in AI technology to identify crucial factors that influence the success of crowdfunding campaigns and to improve their fundraising outcomes by strategically optimizing these factors. Our best-performing machine learning model accurately predicts the fundraising outcomes of 81.0% of campaigns, primarily based on their textual descriptions. Interpreting the machine learning model allows us to provide actionable suggestions on improving the textual description before launching a campaign. We demonstrate that by augmenting just three aspects of the narrative using a large language model, a campaign becomes more preferable to 83% human evaluators, and its likelihood of securing financial support increases by 11.9%. Our research uncovers the effective strategies for crafting descriptions for small business fundraising campaigns and opens up a new realm in integrating large language models into crowdfunding methodologies.

Dating expert Cher Gopman shares how to find love in the new year on'Fox & Friends.' Being single on Valentine's Day can be annoying for some people -- but so can dating. And at a time when online dating is the new norm, experts say there are easier ways to drum up love without swiping for it. Dr. Susan Albersis, a psychologist at Cleveland Clinic in Ohio, told Fox News Digital in a statement that online dating is a "double-edged sword." "On one hand, it creates wonderful connections," she said. "The downside is that it can often bruise your self-esteem."

OpenAI has released the Chat Generative Pre-trained Transformer (ChatGPT) and revolutionized the approach in artificial intelligence to human-model interaction. Several publications on ChatGPT evaluation test its effectiveness on well-known natural language processing (NLP) tasks. However, the existing studies are mostly non-automated and tested on a very limited scale. In this work, we examined ChatGPT's capabilities on 25 diverse analytical NLP tasks, most of them subjective even to humans, such as sentiment analysis, emotion recognition, offensiveness, and stance detection. In contrast, the other tasks require more objective reasoning like word sense disambiguation, linguistic acceptability, and question answering. We also evaluated GPT-4 model on five selected subsets of NLP tasks. We automated ChatGPT and GPT-4 prompting process and analyzed more than 49k responses. Our comparison of its results with available State-of-the-Art (SOTA) solutions showed that the average loss in quality of the ChatGPT model was about 25% for zero-shot and few-shot evaluation. For GPT-4 model, a loss for semantic tasks is significantly lower than for ChatGPT. We showed that the more difficult the task (lower SOTA performance), the higher the ChatGPT loss. It especially refers to pragmatic NLP problems like emotion recognition. We also tested the ability to personalize ChatGPT responses for selected subjective tasks via Random Contextual Few-Shot Personalization, and we obtained significantly better user-based predictions. Additional qualitative analysis revealed a ChatGPT bias, most likely due to the rules imposed on human trainers by OpenAI. Our results provide the basis for a fundamental discussion of whether the high quality of recent predictive NLP models can indicate a tool's usefulness to society and how the learning and validation procedures for such systems should be established.

Our work aims to facilitate by Thrash and Elliot as possessing three core such encounters by providing tools for automatic identification characteristics: evocation (i.e., it is triggered rather than of text content likely to be judged inspiring. We focus on willed), transcendence (i.e., it orients towards things outside inspiration in everyday content as judged by lay people, similar of and greater than the self), and approach motivation (i.e., it in spirit to early work by Hart who attempted to capture the energizes approach rather than avoidance [1]-[3]). Inspiration experience of inspiration in ordinary life [5], rather than "as if has two distinct stages: one an activation state that is more akin it were reserved for the gifted artist, the breakthrough scientist, to feeling and emotion, the second an urge to act.

Do you run a software company looking to reach an audience of early-adopters? For the past few weeks, I've been using GPT-3 to help me with personal development. I wanted to see if it could help me understand issues in my life better, pull out patterns in my thinking, help me bring more gratitude into my life, and clarify my values. I've been journaling for 10 years, and I can attest that using AI is journaling on steroids. To understand what it's like, think of a continuum plotting levels of support you might get from different interactions: Talking to GPT-3 has a lot of the same benefits of journaling: it creates a written record, it never gets tired of listening to you talk, and it's available day or night.