If you have a dog, you might think you have a strong connection with them. But according to a new study, you've probably been reading your pet's emotions all wrong. Although humans and dogs have a unique bond, scientists from Arizona State University say that we are terrible at understanding canine emotions. Participants were shown videos of a dog reacting to positive situations, such as seeing their lead, or negative situations such as being presented with the dreaded vacuum cleaner. Instead of actually trying to understand what the dog is feeling, the researchers found that people tend to'project human emotions onto their pets'.

This work investigates the capabilities of large language models (LLMs) in detecting and understanding human emotions through text. Drawing upon emotion models from psychology, we adopt an interdisciplinary perspective that integrates computational and affective sciences insights. The main goal is to assess how accurately they can identify emotions expressed in textual interactions and compare different models on this specific task. This research contributes to broader efforts to enhance human-computer interaction, making artificial intelligence technologies more responsive and sensitive to users' emotional nuances. By employing a methodology that involves comparisons with a state-of-the-art model on the GoEmotions dataset, we aim to gauge LLMs' effectiveness as a system for emotional analysis, paving the way for potential applications in various fields that require a nuanced understanding of human language.

A cult expert lifted the veil on the "Zizian" fringe group that is linked to the Vermont U.S. Border Patrol agent shooting. The "Zizians" are named for a 34-year-old computer engineer, Jack Amadeus LaSota, who goes by the nickname "Ziz," according to the San Francisco Chronicle. LaSota, who is transgender, goes by female pronouns and created the group of vegan activists, the outlet reported. The group, which began on the West Coast, was launched into the national spotlight after the killing of U.S. Border Patrol Agent David "Chris" Maland in Vermont on Jan. 20. David Maland, a Minnesota native and U.S. Air Force veteran, worked as a Border Patrol agent at the U.S. Customs and Border Protection's Newport Station.

Data sparsity is a key challenge limiting the power of AI tools across various domains. The problem is especially pronounced in domains that require active user input rather than measurements derived from automated sensors. It is a critical barrier to harnessing the full potential of AI in domains requiring active user engagement, such as self-reported mood check-ins, where capturing a continuous picture of emotional states is essential. In this context, sparse data can hinder efforts to capture the nuances of individual emotional experiences such as causes, triggers, and contributing factors. Existing methods for addressing data scarcity often rely on heuristics or large established datasets, favoring deep learning models that lack adaptability to new domains. This paper proposes a novel probabilistic framework that integrates user-centric feedback-based learning, allowing for personalized predictions despite limited data. Achieving 60% accuracy in predicting user states among 64 options (chance of 1/64), this framework effectively mitigates data sparsity. It is versatile across various applications, bridging the gap between theoretical AI research and practical deployment.

Generative AI systems are increasingly capable of expressing emotions via text and imagery. Effective emotional expression will likely play a major role in the efficacy of AI systems -- particularly those designed to support human mental health and wellbeing. This motivates our present research to better understand the alignment of AI expressed emotions with the human perception of emotions. When AI tries to express a particular emotion, how might we assess whether they are successful? To answer this question, we designed a survey to measure the alignment between emotions expressed by generative AI and human perceptions. Three generative image models (DALL-E 2, DALL-E 3 and Stable Diffusion v1) were used to generate 240 examples of images, each of which was based on a prompt designed to express five positive and five negative emotions across both humans and robots. 24 participants recruited from the Prolific website rated the alignment of AI-generated emotional expressions with a text prompt used to generate the emotion (i.e., "A robot expressing the emotion amusement"). The results of our evaluation suggest that generative AI models are indeed capable of producing emotional expressions that are well-aligned with a range of human emotions; however, we show that the alignment significantly depends upon the AI model used and the emotion itself. We analyze variations in the performance of these systems to identify gaps for future improvement. We conclude with a discussion of the implications for future AI systems designed to support mental health and wellbeing.

In addition to proposing the theory of evolution, Darwin studied the expressions and emotions of people and animals. He debated in his writing just how scientific, universal, and predictable emotions actually are, and he sketched characters with exaggerated expressions, which the library had on display. The subject rang a bell for me. Lately, as everyone has been up in arms about ChatGPT, AI general intelligence, and the prospect of robots taking people's jobs, I've noticed that regulators have been ramping up warnings against AI and emotion recognition. Emotion recognition, in this far-from-Darwin context, is the attempt to identify a person's feelings or state of mind using AI analysis of video, facial images, or audio recordings. The idea isn't super complicated: the AI model may see an open mouth, squinted eyes, and contracted cheeks with a thrown-back head, for instance, and register it as a laugh, concluding that the subject is happy.

The emergence of artificial emotional intelligence technology is revolutionizing the fields of computers and robotics, allowing for a new level of communication and understanding of human behavior that was once thought impossible. While recent advancements in deep learning have transformed the field of computer vision, automated understanding of evoked or expressed emotions in visual media remains in its infancy. This foundering stems from the absence of a universally accepted definition of "emotion", coupled with the inherently subjective nature of emotions and their intricate nuances. In this article, we provide a comprehensive, multidisciplinary overview of the field of emotion analysis in visual media, drawing on insights from psychology, engineering, and the arts. We begin by exploring the psychological foundations of emotion and the computational principles that underpin the understanding of emotions from images and videos. We then review the latest research and systems within the field, accentuating the most promising approaches. We also discuss the current technological challenges and limitations of emotion analysis, underscoring the necessity for continued investigation and innovation. We contend that this represents a "Holy Grail" research problem in computing and delineate pivotal directions for future inquiry. Finally, we examine the ethical ramifications of emotion-understanding technologies and contemplate their potential societal impacts. Overall, this article endeavors to equip readers with a deeper understanding of the domain of emotion analysis in visual media and to inspire further research and development in this captivating and rapidly evolving field.

This paper presents an innovative approach to address the problems researchers face in Emotion Aware Recommender Systems (EARS): the difficulty and cumbersome collecting voluminously good quality emotion-tagged datasets and an effective way to protect users' emotional data privacy. Without enough good-quality emotion-tagged datasets, researchers cannot conduct repeatable affective computing research in EARS that generates personalized recommendations based on users' emotional preferences. Similarly, if we fail to fully protect users' emotional data privacy, users could resist engaging with EARS services. This paper introduced a method that detects affective features in subjective passages using the Generative Pre-trained Transformer Technology, forming the basis of the Affective Index and Affective Index Indicator (AII). Eliminate the need for users to build an affective feature detection mechanism. The paper advocates for a separation of responsibility approach where users protect their emotional profile data while EARS service providers refrain from retaining or storing it. Service providers can update users' Affective Indices in memory without saving their privacy data, providing Affective Aware recommendations without compromising user privacy. This paper offers a solution to the subjectivity and variability of emotions, data privacy concerns, and evaluation metrics and benchmarks, paving the way for future EARS research.

Artificial Intelligence poses both risks and rewards, and developers should be weary of "scary" outcomes, AI technologist says. Artificial Intelligence will never be able to truly understand the feeling of some human emotions, a humane technologist told Fox News. "The more integrated AI gets into our lives, the more we will see a difference between human and computer," Alexa Eden, a humane technologist at AlgoAI Tech, told Fox News. "And one of these impenetrable differences will be human emotions, as well as empathy, intuition and other intelligences only humans have. "Empathy is not anything that AI will ever be able to really, truly understand.

AI chatbots are one of the top news stories this year, and it's for a very good reason. Businesses finally recognize the link between good AI technology and good customer service outcomes. So anything that expedites customer service resolutions is a very important step forward for businesses. AI technology doesn't just accelerate customer service solutions; it often leaves a more favorable impression on customers than sometimes murky or ego-based human-to-human interactions. This revolution is so powerful that CNN recently covered the history of chatbot technology.