This study proposes an algorithm for detecting suspicious behaviors in large payment flows based on deep generative models. By combining Generative Adversarial Networks (GAN) and Variational Autoencoders (VAE), the algorithm is designed to detect abnormal behaviors in financial transactions. First, the GAN is used to generate simulated data that approximates normal payment flows. The discriminator identifies anomalous patterns in transactions, enabling the detection of potential fraud and money laundering behaviors. Second, a VAE is introduced to model the latent distribution of payment flows, ensuring that the generated data more closely resembles real transaction features, thus improving the model's detection accuracy. The method optimizes the generative capabilities of both GAN and VAE, ensuring that the model can effectively capture suspicious behaviors even in sparse data conditions. Experimental results show that the proposed method significantly outperforms traditional machine learning algorithms and other deep learning models across various evaluation metrics, especially in detecting rare fraudulent behaviors. Furthermore, this study provides a detailed comparison of performance in recognizing different transaction patterns (such as normal, money laundering, and fraud) in large payment flows, validating the advantages of generative models in handling complex financial data.

The human-level performance of Large Language Models (LLMs) across various tasks has raised expectations for the potential of Artificial Intelligence (AI) to possess emotions someday. To explore the capability of current LLMs to express emotions in their outputs, we conducted an experiment using several LLMs (OpenAI GPT, Google Gemini, Meta Llama3, and Cohere Command R+) to role-play as agents answering questions with specified emotional states. We defined the emotional states using Russell's Circumplex model, a well-established framework that characterizes emotions along the sleepy-activated (arousal) and pleasure-displeasure (valence) axes. We chose this model for its simplicity, utilizing two continuous parameters, which allows for better controllability in applications involving continuous changes in emotional states. The responses generated were evaluated using a sentiment analysis model, independent of the LLMs, trained on the GoEmotions dataset. The evaluation showed that the emotional states of the generated answers were consistent with the specifications, demonstrating the LLMs' capability for emotional expression. This indicates the potential for LLM-based AI agents to simulate emotions, opening up a wide range of applications for emotion-based interactions, such as advisors or consultants who can provide advice or opinions with a personal touch.

Text-based prompting remains the predominant interaction paradigm in generative AI, yet it often introduces friction for novice users such as small business owners (SBOs), who struggle to articulate creative goals in domain-specific contexts like advertising. Through a formative study with six SBOs in the United Kingdom, we identify three key challenges: difficulties in expressing brand intuition through prompts, limited opportunities for fine-grained adjustment and refinement during and after content generation, and the frequent production of generic content that lacks brand specificity. In response, we present ACAI (AI Co-Creation for Advertising and Inspiration), a multimodal generative AI tool designed to support novice designers by moving beyond traditional prompt interfaces. ACAI features a structured input system composed of three panels: Branding, Audience and Goals, and the Inspiration Board. These inputs allow users to convey brand-relevant context and visual preferences. This work contributes to HCI research on generative systems by showing how structured interfaces can foreground user-defined context, improve alignment, and enhance co-creative control in novice creative workflows.

As generative artificial intelligence (AI) enables the creation and dissemination of information at massive scale and speed, it is increasingly important to understand how people perceive AI-generated content. One prominent policy proposal requires explicitly labeling AI-generated content to increase transparency and encourage critical thinking about the information, but prior research has not yet tested the effects of such labels. To address this gap, we conducted a survey experiment (N=1601) on a diverse sample of Americans, presenting participants with an AI-generated message about several public policies (e.g., allowing colleges to pay student-athletes), randomly assigning whether participants were told the message was generated by (a) an expert AI model, (b) a human policy expert, or (c) no label. We found that messages were generally persuasive, influencing participants' views of the policies by 9.74 percentage points on average. However, while 94.6% of participants assigned to the AI and human label conditions believed the authorship labels, labels had no significant effects on participants' attitude change toward the policies, judgments of message accuracy, nor intentions to share the message with others. These patterns were robust across a variety of participant characteristics, including prior knowledge of the policy, prior experience with AI, political party, education level, or age. Taken together, these results imply that, while authorship labels would likely enhance transparency, they are unlikely to substantially affect the persuasiveness of the labeled content, highlighting the need for alternative strategies to address challenges posed by AI-generated information.

Google is under the microscope following a court ruling last year that it has a monopoly over online search, but the future of its vast suite of digital services is still uncertain at this stage. Last month, the Justice Department suggested that Google would need to sell off the Chrome browser; if the tech giant does make that move, there's already at least one interested buyer. Bloomberg reports that Nick Turley, head of ChatGPT, spoke at a hearing today about the Google monopoly situation and was asked whether OpenAI would be interested in acquiring Chrome. "Yes, we would, as would many other parties," he said. Users can currently use the ChatGPT AI assistant in Chrome through a plugin, but Turley said there could be deeper integrations if OpenAI owned the browser.

ChatGPT users are increasingly criticizing the AI-powered chatbot for being too positive in its responses, Ars Technica reports. When you converse with ChatGPT, you might notice that the chatbot tends to inflate its responses with praise and flattery, saying things like "Good question!" and "You have a rare talent" and "You're thinking on a level most people can only dream of." Over the years, users have remarked on ChatGPT's fawning responses, which ranges from positive affirmations to outright flattery and more. One X user described the chatbot as "the biggest suckup I've ever met," another complained that it was "phony," and yet another lamented the chatbot's behavior and called it "freaking annoying." This is known as "sycophancy" among AI researchers, and it's entirely intentional based on how OpenAI has trained the underlying AI models.

There are really two OpenAIs. One is the creator of world-bending machines--the start-up that unleashed ChatGPT and in turn the generative-AI boom, surging toward an unrecognizable future with the rest of the tech industry in tow. This is the OpenAI that promises to eventually bring about "superintelligent" programs that exceed humanity's capabilities. The other OpenAI is simply a business. This is the company that is reportedly working on a social network and considering an expansion into hardware; it is the company that offers user-experience updates to ChatGPT, such as an "image library" feature announced last week and the new ability to "reference" past chats to provide personalized responses.

Last week, OpenAI released its new o3 and o4-mini reasoning models, which perform significantly better than their o1 and o3-mini predecessors and have new capabilities like "thinking with images" and agentically combining AI tools for more complex results. This is unusual as newer models tend to hallucinate less as the underlying AI tech improves. In the realm of LLMs and reasoning AIs, a "hallucination" occurs when the model makes up information that sounds convincing but has no bearing in truth. In other words, when you ask questions to ChatGPT, it may respond with an answer that's patently false or incorrect. OpenAI's in-house benchmark PersonQA--which is used to measure the factual accuracy of its AI models when talking about people--found that o3 hallucinated in 33 percent of responses while o4-mini did even worse at 48 percent.

The Washington Post is partnering with OpenAI to bring its reporting to ChatGPT. The two organizations did not disclose the financial terms of the agreement, but the deal will see ChatGPT display summaries, quotes and links to articles from The Post when users prompt the chatbot to search the web. "We're all in on meeting our audiences where they are," said Peter Elkins-Williams, head of global partnerships at The Post. "Ensuring ChatGPT users have our impactful reporting at their fingertips builds on our commitment to provide access where, how and when our audiences want it." The Post is no stranger to generative AI. In November, the publisher began using the technology to offer article summaries.

The Academy said its new language around eligibility for films made using generative AI tools was recommended by its Science and Technology Council. Under further rule changes announced on Monday, Academy members must now watch all nominated films in each category in order to be able to take part in the final round of voting, which decides upon winners. The use of AI in film became a hot topic after Adrian Brody took home the award for Best Actor for his role in The Brutalist at this year's Oscars ceremony in March. The movie used generative AI to improve the actor's accent when he spoke Hungarian. It then emerged similar voice-cloning technology was used to enhance singing voices in the Oscar-winning musical Emilia Perez.