W.J.T. Mitchell's influential essay 'What do pictures want?' shifts the theoretical focus away from the interpretative act of understanding pictures and from the motivations of the humans who create them to the possibility that the picture itself is an entity with agency and wants. In this article, I reframe Mitchell's question in light of contemporary AI image generation tools to ask: what do AI-generated images want? Drawing from art historical discourse on the nature of abstraction, I argue that AI-generated images want specificity and concreteness because they are fundamentally abstract. Multimodal text-to-image models, which are the primary subject of this article, are based on the premise that text and image are interchangeable or exchangeable tokens and that there is a commensurability between them, at least as represented mathematically in data. The user pipeline that sees textual input become visual output, however, obscures this representational regress and makes it seem like one form transforms into the other -- as if by magic.

Typology is a subfield of linguistics that focuses on the study and classification of languages based on their structural features. Unlike genealogical classification, which examines the historical relationships between languages, typology seeks to understand the diversity of human languages by identifying common properties and patterns, known as universals. In recent years, computational methods have played an increasingly important role in typological research, enabling the analysis of large-scale linguistic data and the testing of hypotheses about language structure and evolution. This article provides an illustration of the benefits of computational statistical modeling in typology.

Body Dysmorphic Disorder (BDD) is a highly prevalent and frequently underdiagnosed condition characterized by persistent, intrusive preoccupations with perceived defects in physical appearance. In this extended analysis, we employ multiple machine learning approaches to predict treatment outcomes -- specifically treatment response and remission -- with an emphasis on interpretability to ensure clinical relevance and utility. Across the various models investigated, treatment credibility emerged as the most potent predictor, surpassing traditional markers such as baseline symptom severity or comorbid conditions. Notably, while simpler models (e.g., logistic regression and support vector machines) achieved competitive predictive performance, decision tree analyses provided unique insights by revealing clinically interpretable threshold values in credibility scores. These thresholds can serve as practical guideposts for clinicians when tailoring interventions or allocating treatment resources. We further contextualize our findings within the broader literature on BDD, addressing technology-based therapeutics, digital interventions, and the psychosocial determinants of treatment engagement. An extensive array of references situates our results within current research on BDD prevalence, suicidality risks, and digital innovation. Our work underscores the potential of integrating rigorous statistical methodologies with transparent machine learning models. By systematically identifying modifiable predictors -- such as treatment credibility -- we propose a pathway toward more targeted, personalized, and ultimately efficacious interventions for individuals with BDD.

The North Carolina Highway Patrol has three rotating artificial intelligence devices to help track down distracted commercial vehicles. North Carolina Highway Patrol reports that it has seen an uptick in distracted truck drivers, and now the agency is using artificial intelligence devices to help crack down on the safety hazard. Distracted driving killed over 3,500 people in 2021, according to the U.S. Department of Transportation. A mom who's made safe driving her passion has felt the pain from a distracted driver two separate times. "At a stop light you look around, every single person is on their phone," said Jennifer Smith, whose mother was killed by a distracted driver.

Emotions are experienced and expressed through various response systems. Coherence between emotional experience and emotional expression is considered important to clients' well being. To date, emotional coherence (EC) has been studied at a single time point using lab-based tasks with relatively small datasets. No study has examined EC between the subjective experience of emotions and emotion expression in therapy or whether this coherence is associated with clients' well being. Natural language Processing (NLP) approaches have been applied to identify emotions from psychotherapy dialogue, which can be implemented to study emotional processes on a larger scale. However, these methods have yet to be used to study coherence between emotional experience and emotional expression over the course of therapy and whether it relates to clients' well-being. This work presents an end-to-end approach where we use emotion predictions from our transformer based emotion recognition model to study emotional coherence and its diagnostic potential in psychotherapy research. We first employ our transformer based approach on a Hebrew psychotherapy dataset to automatically label clients' emotions at utterance level in psychotherapy dialogues. We subsequently investigate the emotional coherence between clients' self-reported emotional states and our model-based emotion predictions. We also examine the association between emotional coherence and clients' well being. Our findings indicate a significant correlation between clients' self-reported emotions and positive and negative emotions expressed verbally during psychotherapy sessions. Coherence in positive emotions was also highly correlated with clients well-being. These results illustrate how NLP can be applied to identify important emotional processes in psychotherapy to improve diagnosis and treatment for clients suffering from mental-health problems.

Flexible metadata pipelines are crucial for supporting the FAIR data principles. Despite this need, researchers seldom report their approaches for identifying metadata standards and protocols that support optimal flexibility. This paper reports on an initiative targeting the development of a flexible metadata pipeline for a collection containing over 300,000 digital fish specimen images, harvested from multiple data repositories and fish collections. The images and their associated metadata are being used for AI-related scientific research involving automated species identification, segmentation and trait extraction. The paper provides contextual background, followed by the presentation of a four-phased approach involving: 1. Assessment of the Problem, 2. Investigation of Solutions, 3. Implementation, and 4. Refinement. The work is part of the NSF Harnessing the Data Revolution, Biology Guided Neural Networks (NSF/HDR-BGNN) project and the HDR Imageomics Institute. An RDF graph prototype pipeline is presented, followed by a discussion of research implications and conclusion summarizing the results.

"AI tools will be shaping the future of customer service -- this seems to be the way the world is heading, with technology becoming more and more integrated into business tactics," said Sourabh Gupta, co-founder and CEO of Skit.ai. Nirmal emphasized that this technology is ultimately designed for a person trying to access information, solve a problem, pay a bill or schedule an appointment. Virgil Wong, chief digital officer at HGS, told CMSWire AI customers want well-designed chatbot interactions -- especially ones that can offer quick solutions to problems. He also added that "sentiment analysis is critical to determining the next best action, getting the correct answer fast, detecting fraud or compliance issues and summarizing interactions for follow-ups and reporting." According to Wong, some of the biggest issues brands come up against with this technology include strategic considerations, such as how much to invest, along with technical concerns, like bugs and program errors.

Emotional intelligence is a crucial aspect of customer communication, and key to deliver exceptional customer experiences. Today, leveraging automation in the sales, marketing and customer service arena while balancing the need for empathy is critical. While empathy is seen as a uniquely human trait, developments in artificial intelligence (AI) to help brands recognize and respond in an empathetic manner are on the rise and come at a critical time. Studies show that human empathy is on the decline – a deficit costing the average brand $300 million in lost revenue every year. For brands to truly connect with their customers to effectively market, sell and serve, they need to really understand the customer mindset. This is where empathic AI solutions are coming into the picture to complement and empower sales, marketing, and service professionals on the customer front lines to detect the customer's emotional state and intent and deliver a better customer experience.

Ever wanted to have Leo Tolstoy's Anna Karenina or Franz Kafka's Metamorphosis read to you by trap god Gucci Mane, creator of such hits as "Lemonade" and "Wasted"? Project Gucciberg is the latest drop from viral factory MSCHF, and it does exactly that. Using machine learning, MSCHF created an audio deepfake of Gucci Mane reading a selection of classic texts from Little Women to Beowulf. They're all free to listen to and come with book covers that blend in perfectly with the artwork of Gucci Mane's prolific discography. The what of Project Gucciberg is luridly straightforward, but the why is harder to answer.

At least one future is here right now. The prankster art / marketing collective MSCHF recently spent $74,500 to purchase a Spot robo-dog from Boston Dynamics. It mounted a Tippmann 98 paintball gun on its back and is allowing people around the world to remotely control the bot via their phones in an art gallery filled with its own work for two minutes at a time. MSCHF is calling it Spot's Rampage, and the event is happening on February 24th at 1PM ET. When killer robots come to America they will be wrapped in fur, carrying a ball.