Where writing is concerned, the best of today's AIs can be very, very good. A few years ago, a text generator called GPT-2 analyzed a sample of writing by Harvard psychologist Steven Pinker, then produced an imitation that hardly anyone could distinguish from the real thing. A more recent AI called Copilot, which has been customized for programming uses, is speeding up the work of practiced coders–it sometimes knows more than they do. A sample from a writing assistant called Jasper (formerly known as Jarvis) struck an editor as better than the work of some professional writers. The machines seem to have a particular knack for conversations. This may not be writing per se, but it's a language challenge that leaves some humans floundering.

A hoverbike that can travel at 62 miles per hour for up to 40 minutes made its U.S. debut this week at the North American Auto Show in Detroit. The flying bike is the work of Aerwins, a Delaware-based company that makes drones and unmanned vehicles. Although it conjures up futuristic Jetsons visions of s oaring high above New York City's notoriously clogged streets, you probably won't be riding the hoverbike out to John F. Kennedy Airport anytime soon. The Xturismo currently costs $777,000, although Aerwins says it will develop a smaller model next year, as well as an all-electric model in 2025 to sell for about $50,000. 'I feel like I'm literally 15-years-old and I just got out of Star Wars and I jumped on their bike,' Thad Scott, co-chair of the auto show, told Reuters.

The global automotive artificial intelligence market size is projected to grow from USD 2.3 Billion in 2022 to USD 7.0 Billion by 2027, …

It uses artificial intelligence to analyze toddlers' movements, eye positions, and facial expressions, among other things, to help predict who has …

Artificial intelligence also known as machine intelligence is a branch of computer science that works to create intelligent machines.

… is the first of its kind to be awarded at UTSA and will support Sooby’s research on utilizing artificial intelligence (AI) to study complex …

Minority groups have been using social media to organize social movements that create profound social impacts. Black Lives Matter (BLM) and Stop Asian Hate (SAH) are two successful social movements that have spread on Twitter that promote protests and activities against racism and increase the public's awareness of other social challenges that minority groups face. However, previous studies have mostly conducted qualitative analyses of tweets or interviews with users, which may not comprehensively and validly represent all tweets. Very few studies have explored the Twitter topics within BLM and SAH dialogs in a rigorous, quantified and data-centered approach. Therefore, in this research, we adopted a mixed-methods approach to comprehensively analyze BLM and SAH Twitter topics. We implemented (1) the latent Dirichlet allocation model to understand the top high-level words and topics and (2) open-coding analysis to identify specific themes across the tweets. We collected more than one million tweets with the #blacklivesmatter and #stopasianhate hashtags and compared their topics. Our findings revealed that the tweets discussed a variety of influential topics in depth, and social justice, social movements, and emotional sentiments were common topics in both movements, though with unique subtopics for each movement. Our study contributes to the topic analysis of social movements on social media platforms in particular and the literature on the interplay of AI, ethics, and society in general.

As social media becomes a hotbed for the spread of misinformation, the crucial task of rumor detection has witnessed promising advances fostered by open-source benchmark datasets. Despite being widely used, we find that these datasets suffer from spurious correlations, which are ignored by existing studies and lead to severe overestimation of existing rumor detection performance. The spurious correlations stem from three causes: (1) event-based data collection and labeling schemes assign the same veracity label to multiple highly similar posts from the same underlying event; (2) merging multiple data sources spuriously relates source identities to veracity labels; and (3) labeling bias. In this paper, we closely investigate three of the most popular rumor detection benchmark datasets (i.e., Twitter15, Twitter16 and PHEME), and propose event-separated rumor detection as a solution to eliminate spurious cues. Under the event-separated setting, we observe that the accuracy of existing state-of-the-art models drops significantly by over 40%, becoming only comparable to a simple neural classifier. To better address this task, we propose Publisher Style Aggregation (PSA), a generalizable approach that aggregates publisher posting records to learn writing style and veracity stance. Extensive experiments demonstrate that our method outperforms existing baselines in terms of effectiveness, efficiency and generalizability.

In this work, the integration of two machine learning approaches, namely domain adaptation and explainable AI, is proposed to address these two issues of generalized detection and explainability. Firstly the Domain Adversarial Neural Network (DANN) develops a generalized misinformation detector across multiple social media platforms DANN is employed to generate the classification results for test domains with relevant but unseen data. The DANN-based model, a traditional black-box model, cannot justify its outcome, i.e., the labels for the target domain. Hence a Local Interpretable Model-Agnostic Explanations (LIME) explainable AI model is applied to explain the outcome of the DANN mode. To demonstrate these two approaches and their integration for effective explainable generalized detection, COVID-19 misinformation is considered a case study. We experimented with two datasets, namely CoAID and MiSoVac, and compared results with and without DANN implementation. DANN significantly improves the accuracy measure F1 classification score and increases the accuracy and AUC performance. The results obtained show that the proposed framework performs well in the case of domain shift and can learn domain-invariant features while explaining the target labels with LIME implementation enabling trustworthy information processing and extraction to combat misinformation effectively.

The study of the ethical impact of AI and the design of trustworthy systems needs the analysis of the scenarios where AI systems are used, which is related to the software engineering concept of "use case" and the "intended purpose" legal term. However, there is no standard methodology for use case documentation covering the context of use, scope, functional requirements and risks of an AI system. In this work, we propose a novel documentation methodology for AI use cases, with a special focus on the affective computing domain. Our approach builds upon an assessment of use case information needs documented in the research literature and the recently proposed European regulatory framework for AI. From this assessment, we adopt and adapt the Unified Modeling Language (UML), which has been used in the last two decades mostly by software engineers. Each use case is then represented by an UML diagram and a structured table, and we provide a set of examples illustrating its application to several affective computing scenarios.