Academic researchers and social media entities grappling with the identification of hate speech face significant challenges, primarily due to the vast scale of data and the dynamic nature of hate speech. Given the ethical and practical limitations of large predictive models like ChatGPT in directly addressing such sensitive issues, our research has explored alternative advanced transformer-based and generative AI technologies since 2019. Specifically, we developed a new data labeling technique and established a proof of concept targeting anti-Semitic hate speech, utilizing a variety of transformer models such as BERT (arXiv:1810.04805), DistillBERT (arXiv:1910.01108), RoBERTa (arXiv:1907.11692), and LLaMA-2 (arXiv:2307.09288), complemented by the LoRA fine-tuning approach (arXiv:2106.09685). This paper delineates and evaluates the comparative efficacy of these cutting-edge methods in tackling the intricacies of hate speech detection, highlighting the need for responsible and carefully managed AI applications within sensitive contexts.

Reinforcing or even exacerbating societal biases and inequalities will increase significantly as generative AI increasingly produces useful artifacts, from text to images and beyond, for the real world. We address these issues by formally characterizing the notion of fairness for generative AI as a basis for monitoring and enforcing fairness. We define two levels of fairness using the notion of infinite sequences of abstractions of AI-generated artifacts such as text or images. The first is the fairness demonstrated on the generated sequences, which is evaluated only on the outputs while agnostic to the prompts and models used. The second is the inherent fairness of the generative AI model, which requires that fairness be manifested when input prompts are neutral, that is, they do not explicitly instruct the generative AI to produce a particular type of output. We also study relative intersectional fairness to counteract the combinatorial explosion of fairness when considering multiple categories together with lazy fairness enforcement. Finally, fairness monitoring and enforcement are tested against some current generative AI models.

Diffusion models have exhibit exceptional performance in text-to-image generation and editing. However, existing methods often face challenges when handling complex text prompts that involve multiple objects with multiple attributes and relationships. In this paper, we propose a brand new training-free text-to-image generation/editing framework, namely Recaption, Plan and Generate (RPG), harnessing the powerful chain-of-thought reasoning ability of multimodal LLMs to enhance the compositionality of text-to-image diffusion models. Our approach employs the MLLM as a global planner to decompose the process of generating complex images into multiple simpler generation tasks within subregions. We propose complementary regional diffusion to enable region-wise compositional generation. Furthermore, we integrate text-guided image generation and editing within the proposed RPG in a closed-loop fashion, thereby enhancing generalization ability. Extensive experiments demonstrate our RPG outperforms state-of-the-art text-to-image diffusion models, including DALL-E 3 and SDXL, particularly in multi-category object composition and text-image semantic alignment. Notably, our RPG framework exhibits wide compatibility with various MLLM architectures (e.g., MiniGPT-4) and diffusion backbones (e.g., ControlNet). Our code is available at: https://github.com/YangLing0818/RPG-DiffusionMaster

In 2022, 255 billion new app downloads were registered, and a whopping 167 billion USD was spent on app stores, a drastic increase from 230 billion app downloads in 2021. Interestingly, artificial intelligence is projected to increase mobile app downloads by 10% in 2024. To continue fueling their revenues in a highly competitive and volatile market, mobile app companies need to dedicate significant efforts to the design of user-friendly interfaces and the usability of their applications. Usability testing of mobile applications is inherently a complex and expensive process [1], yet rewarding in elaborating user requirements, identifying usability issues, and improving the quality of user experience [2]. Mobile usability testing encompasses several intertwined and laborious phases, including planning and designing the evaluation sessions, recruiting the intended users, conducting the testing sessions, and analyzing testing data to extract actionable insights [1].

Generative Artificial Intelligence (AI) is integrated into everyday technology, including news, education, and social media. AI has further pervaded private conversations as conversational partners, auto-completion, and response suggestions. As social media becomes young people's main method of peer support exchange, we need to understand when and how AI can facilitate and assist in such exchanges in a beneficial, safe, and socially appropriate way. We asked 622 young people to complete an online survey and evaluate blinded human- and AI-generated responses to help-seeking messages. We found that participants preferred the AI-generated response to situations about relationships, self-expression, and physical health. However, when addressing a sensitive topic, like suicidal thoughts, young people preferred the human response. We also discuss the role of training in online peer support exchange and its implications for supporting young people's well-being. Disclaimer: This paper includes sensitive topics, including suicide ideation. Reader discretion is advised.

As generative artificial intelligence (AI), particularly Large Language Models (LLMs), continues to permeate healthcare, it remains crucial to supplement traditional automated evaluations with human expert evaluation. Understanding and evaluating the generated texts is vital for ensuring safety, reliability, and effectiveness. However, the cumbersome, time-consuming, and non-standardized nature of human evaluation presents significant obstacles to the widespread adoption of LLMs in practice. This study reviews existing literature on human evaluation methodologies for LLMs within healthcare. We highlight a notable need for a standardized and consistent human evaluation approach. Our extensive literature search, adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, spans publications from January 2018 to February 2024. This review provides a comprehensive overview of the human evaluation approaches used in diverse healthcare applications.This analysis examines the human evaluation of LLMs across various medical specialties, addressing factors such as evaluation dimensions, sample types, and sizes, the selection and recruitment of evaluators, frameworks and metrics, the evaluation process, and statistical analysis of the results. Drawing from diverse evaluation strategies highlighted in these studies, we propose a comprehensive and practical framework for human evaluation of generative LLMs, named QUEST: Quality of Information, Understanding and Reasoning, Expression Style and Persona, Safety and Harm, and Trust and Confidence. This framework aims to improve the reliability, generalizability, and applicability of human evaluation of generative LLMs in different healthcare applications by defining clear evaluation dimensions and offering detailed guidelines.

Anomaly detection is critical for the secure and reliable operation of industrial control systems. As our reliance on such complex cyber-physical systems grows, it becomes paramount to have automated methods for detecting anomalies, preventing attacks, and responding intelligently. {This paper presents a novel deep generative model to meet this need. The proposed model follows a variational autoencoder architecture with a convolutional encoder and decoder to extract features from both spatial and temporal dimensions. Additionally, we incorporate an attention mechanism that directs focus towards specific regions, enhancing the representation of relevant features and improving anomaly detection accuracy. We also employ a dynamic threshold approach leveraging the reconstruction probability and make our source code publicly available to promote reproducibility and facilitate further research. Comprehensive experimental analysis is conducted on data from all six stages of the Secure Water Treatment (SWaT) testbed, and the experimental results demonstrate the superior performance of our approach compared to several state-of-the-art baseline techniques.

In recent years, generative artificial intelligence models, represented by Large Language Models (LLMs) and Diffusion Models (DMs), have revolutionized content production methods. These artificial intelligence-generated content (AIGC) have become deeply embedded in various aspects of daily life and work. However, these technologies have also led to the emergence of Fake Artificial Intelligence Generated Content (FAIGC), posing new challenges in distinguishing genuine information. It is crucial to recognize that AIGC technology is akin to a double-edged sword; its potent generative capabilities, while beneficial, also pose risks for the creation and dissemination of FAIGC. In this survey, We propose a new taxonomy that provides a more comprehensive breakdown of the space of FAIGC methods today. Next, we explore the modalities and generative technologies of FAIGC. We introduce FAIGC detection methods and summarize the related benchmark from various perspectives. Finally, we discuss outstanding challenges and promising areas for future research.

We conducted an in-person ethnography in India and the US to investigate how young people (18-24) trusted online content, with a focus on generative AI (GenAI). We had four key findings about how young people use GenAI and determine what to trust online. First, when online, we found participants fluidly shifted between mindsets and emotional states, which we term "information modes." Second, these information modes shaped how and why participants trust GenAI and how they applied literacy skills. In the modes where they spent most of their time, they eschewed literacy skills. Third, with the advent of GenAI, participants imported existing trust heuristics from familiar online contexts into their interactions with GenAI. Fourth, although study participants had reservations about GenAI, they saw it as a requisite tool to adopt to keep up with the times. Participants valued efficiency above all else, and used GenAI to further their goals quickly at the expense of accuracy. Our findings suggest that young people spend the majority of their time online not concerned with truth because they are seeking only to pass the time. As a result, literacy interventions should be designed to intervene at the right time, to match users' distinct information modes, and to work with their existing fact-checking practices.

Emails from the Department of Justice's antitrust case against Google revealed how Microsoft executives were alarmed by and even envious of Google's AI lead. In an email thread, CTO Kevin Scott wrote he was "very, very worried" about Google's rapidly growing AI capabilities. He said he initially dismissed the company's "game-playing stunts," likely referring to Google's AlphaGo models. The emails reference Gmail's autocomplete features, which execs called "scary good." Microsoft struggled to copy Google's BERT-large, an AI model that deciphers the meaning and context of words in a sentence.