The learning rate is a crucial hyperparameter in deep learning, with its ideal value depending on the problem and potentially changing during training. In this paper, we investigate the practical utility of adaptive learning rate mechanisms that adjust step sizes dynamically in response to the loss landscape. We revisit a cumulative path-based adaptation scheme proposed in 2017, which adjusts the learning rate based on the discrepancy between the observed path length, computed as a time-discounted sum of normalized gradient steps, and the expected length of a random walk. While the original approach offers a compelling intuition, we show that its adaptation mechanism for Adam is conceptually inconsistent due to the optimizer's internal preconditioning. We propose a corrected variant that better reflects Adam's update dynamics. To assess the practical value of online learning rate adaptation, we benchmark SGD and Adam, with and without cumulative adaptation, and compare them to a recent alternative method. Our results aim to clarify when and why such adaptive strategies offer practical benefits.

Stories are central to human culture, serving to share ideas, preserve traditions, and foster connections. Automatic story generation, a key advancement in artificial intelligence (AI), offers new possibilities for creating personalized content, exploring creative ideas, and enhancing interactive experiences. However, existing methods struggle to maintain narrative coherence and logical consistency. This disconnect compromises the overall storytelling experience, underscoring the need for substantial improvements. Inspired by human cognitive processes, we introduce Storyteller, a novel approach that systemically improves the coherence and consistency of automatically generated stories. Storyteller introduces a plot node structure based on linguistically grounded subject verb object (SVO) triplets, which capture essential story events and ensure a consistent logical flow. Unlike previous methods, Storyteller integrates two dynamic modules, the STORYLINE and narrative entity knowledge graph (NEKG),that continuously interact with the story generation process. This integration produces structurally sound, cohesive and immersive narratives. Extensive experiments demonstrate that Storyteller significantly outperforms existing approaches, achieving an 84.33% average win rate through human preference evaluation. At the same time, it is also far ahead in other aspects including creativity, coherence, engagement, and relevance.

Study of affect in speech requires suitable data, as emotional expression and perception vary across languages. Until now, no corpus has existed for natural expression of affect in spontaneous Finnish, existing data being acted or from a very specific communicative setting. This paper presents the first such corpus, created by annotating 12,000 utterances for emotional arousal and valence, sampled from three large-scale Finnish speech corpora. To ensure diverse affective expression, sample selection was conducted with an affect mining approach combining acoustic, cross-linguistic speech emotion, and text sentiment features. We compare this method to random sampling in terms of annotation diversity, and conduct post-hoc analyses to identify sampling choices that would have maximized the diversity. As an outcome, the work introduces a spontaneous Finnish affective speech corpus and informs sampling strategies for affective speech corpus creation in other languages or domains.

Minimally invasive procedure at the Mayo Clinic uses NeuroOne's cutting-edge brain implant technology. Imagine waking up seizure-free after years of suffering. For 17-year-old Clara Fuller, this dream became reality thanks to groundbreaking brain implant technology. Her journey from relentless seizures to a normal teenage life highlights the incredible potential of medical innovation. GET SECURITY ALERTS & EXPERT TECH TIPS – SIGN UP FOR KURT'S'THE CYBERGUY REPORT' NOW At just 13, Clara began experiencing uncontrollable seizures that baffled doctors.

Turn-taking is a fundamental aspect of conversation, but current Human-Robot Interaction (HRI) systems often rely on simplistic, silence-based models, leading to unnatural pauses and interruptions. This paper investigates, for the first time, the application of general turn-taking models, specifically TurnGPT and Voice Activity Projection (VAP), to improve conversational dynamics in HRI. These models are trained on human-human dialogue data using self-supervised learning objectives, without requiring domain-specific fine-tuning. We propose methods for using these models in tandem to predict when a robot should begin preparing responses, take turns, and handle potential interruptions. We evaluated the proposed system in a within-subject study against a traditional baseline system, using the Furhat robot with 39 adults in a conversational setting, in combination with a large language model for autonomous response generation. The results show that participants significantly prefer the proposed system, and it significantly reduces response delays and interruptions.

Causal reasoning, in particular, counterfactual reasoning plays a central role in testing for discrimination. Counterfactual reasoning materializes when testing for discrimination, what is known as the counterfactual model of discrimination, when we compare the discrimination comparator with the discrimination complainant, where the comparator is a similar (or similarly situated) profile to that of the complainant used for testing the discrimination claim of the complainant. In this paper, we revisit the comparator by presenting two kinds of comparators based on the sort of causal intervention we want to represent. We present the ceteris paribus and the mutatis mutandis comparator, where the former is the standard and the latter is a new kind of comparator. We argue for the use of the mutatis mutandis comparator, which is built on the fairness given the difference notion, for testing future algorithmic discrimination cases.

Multilingual speech processing requires understanding emotions, a task made difficult by limited labelled data. CLARA, minimizes reliance on labelled data, enhancing generalization across languages. It excels at fostering shared representations, aiding cross-lingual transfer of speech and emotions, even with little data. Our approach adeptly captures emotional nuances in speech, overcoming subjective assessment issues. Using a large multilingual audio corpus and self-supervised learning, CLARA develops speech representations enriched with emotions, advancing emotion-aware multilingual speech processing. Our method expands the data range using data augmentation, textual embedding for visual understanding, and transfers knowledge from high- to low-resource languages. CLARA demonstrates excellent performance in emotion recognition, language comprehension, and audio benchmarks, excelling in zero-shot and few-shot learning. It adapts to low-resource languages, marking progress in multilingual speech representation learning.

Offering advanced technology content on vehicles often results in a steep increase in problems experienced, according to the J.D. Power 2022 U.S. Tech Experience Index (TXI) Study, released . Vehicle quality, as expressed in problems per 100 vehicles (PP100), is a common measure within both the TXI Study, focused on advanced vehicle technology as it first comes to market, and the annual J.D. Power Initial Quality StudySM (IQS). Of the advanced technologies included in the 2022 TXI Study, 46% of them had at least one problem with a PP100 higher than the most problematic attribute included in the 2022 IQS, with some exceeding it several times over. A low PP100 score indicates better quality. AF Group, a nationally recognized holding company whose affiliated brands provide specialty and workers' compensation insurance solutions across the United States, and CLARA Analytics ("CLARA"), the leading provider of artificial intelligence (AI) technology for commercial insurance claims optimization, announced a new initiative to deploy AI technology to improve medical outcomes for injured workers, while also reducing claims losses for the company's workers' compensation policyholders and streamlining claims management.

CLARA Analytics ("CLARA"), the leading provider of artificial intelligence (AI) technology in the commercial insurance industry, announced that it has made two new strategic hires, adding Ram Rangaraj as the company's new Chief Technology Officer and enlisting Mubbin Rabbani as the new Vice President of Product. Ram Rangaraj is a veteran IT leader with over two decades of experience at Kaiser Permanente, where he served in a range of technology leadership roles, most recently as Senior Director of Revenue Management Integration Engineering. During his tenure at Kaiser, he led numerous strategic IT initiatives, including the innovative application of data analytics to improve the company's claims management performance. Rangaraj will lead the evolution and operations of CLARA's AI platform, serving as a core member of the executive leadership team and reporting directly to CLARA CEO Heather H. Wilson. "Ram Rangaraj is an elite performer," said Wilson.

WIRE)--CLARA Analytics ("CLARA"), the leading provider of artificial intelligence (AI) technology in the commercial insurance industry, today announced that it has hired Tyler Jones to be its Chief Customer and Marketing Officer. Jones will be responsible for directing the company's complete relationship with its customers and driving efforts to assess and elevate experiences at each touchpoint across the customer journey, ensuring that commercial insurers are achieving optimal value from CLARA's AI platform. Reporting to CLARA CEO Heather H. Wilson, Jones will apply his unique blend of customer-focused innovation, technology and insurance industry expertise to CLARA's ongoing pursuit of superior customer value, continuous improvement, and exceptional service to its clients. Tyler Jones is a seasoned executive with nearly two decades of experience in the insurance and banking industries. During his tenure at Kaiser Permanente, he was accountable for technology strategies to support the company's revenue management operations.