Generative AI (GenAI) chatbots are becoming increasingly integrated into virtual assistant technologies, yet their success hinges on the ability to gather meaningful user feedback to improve interaction quality, system outcomes, and overall user acceptance. Successful chatbot interactions can enable organizations to build long-term relationships with their customers and users, supporting customer loyalty and furthering the organization's goals. This study explores the impact of two distinct narratives and feedback collection mechanisms on user engagement and feedback behavior: a standard AI-focused interaction versus a hybrid intelligence (HI) framed interaction. Initial findings indicate that while small-scale survey measures allowed for no significant differences in user willingness to leave feedback, use the system, or trust the system, participants exposed to the HI narrative statistically significantly provided more detailed feedback. These initial findings offer insights into designing effective feedback systems for GenAI virtual assistants, balancing user effort with system improvement potential.

The ability of machines to comprehend and produce language that is similar to that of humans has revolutionized sectors like customer service, healthcare, and finance thanks to the quick advances in Natural Language Processing (NLP), which are fueled by Generative Artificial Intelligence (AI) and Large Language Models (LLMs). However, because LLMs trained on large datasets may unintentionally absorb and reveal Personally Identifiable Information (PII) from user interactions, these capabilities also raise serious privacy concerns. Deep neural networks' intricacy makes it difficult to track down or stop the inadvertent storing and release of private information, which raises serious concerns about the privacy and security of AI-driven data. This study tackles these issues by detecting Generative AI weaknesses through attacks such as data extraction, model inversion, and membership inference. A privacy-preserving Generative AI application that is resistant to these assaults is then developed. It ensures privacy without sacrificing functionality by using methods to identify, alter, or remove PII before to dealing with LLMs. In order to determine how well cloud platforms like Microsoft Azure, Google Cloud, and AWS provide privacy tools for protecting AI applications, the study also examines these technologies. In the end, this study offers a fundamental privacy paradigm for generative AI systems, focusing on data security and moral AI implementation, and opening the door to a more secure and conscientious use of these tools.

Finals season looks different this year. Across college campuses, students are slogging their way through exams with all-nighters and lots of caffeine, just as they always have. Through the end of May, OpenAI is offering students two months of free access to ChatGPT Plus, which normally costs 20 a month. It's a compelling deal for students who want help cramming--or cheating--their way through finals: Rather than firing up the free version of ChatGPT to outsource essay writing or work through a practice chemistry exam, students are now able to access the company's most advanced models, as well as its "deep research" tool, which can quickly synthesize hundreds of digital sources into analytical reports. The OpenAI deal is just one of many such AI promotions going around campuses.

The Academy of Motion Picture Arts and Sciences has decide that its official stance towards AI-use in films is to take no stance at all, according to a statement the organization shared outlining changes to voting for the 98th Oscars. The issue of award-nominated films using AI was first raised in 2024 when the productions behind Best Picture nominees The Brutalist and Emilia Pérez admitted to using the tech to alter performances. "With regard to Generative Artificial Intelligence and other digital tools used in the making of the film, the tools neither help nor harm the chances of achieving a nomination, " AMPAS writes. "The Academy and each branch will judge the achievement, taking into account the degree to which a human was at the heart of the creative authorship when choosing which movie to award." While the organization at least reaffirms that human involvement is their primary concern, they also don't seem to believe that using AI -- potentially trained on the ill-gotten work of their membership -- is an existential problem.

As generative AI models produce increasingly realistic output, both academia and industry are focusing on the ability to detect whether an output was generated by an AI model or not. Many of the research efforts and policy discourse are centered around robust watermarking of AI outputs. While plenty of progress has been made, all watermarking and AI detection techniques face severe limitations. In this position paper, we argue that we are adopting the wrong approach, and should instead focus on watermarking via cryptographic signatures trustworthy content rather than AI generated ones. For audio-visual content, in particular, all real content is grounded in the physical world and captured via hardware sensors. This presents a unique opportunity to watermark at the hardware layer, and we lay out a socio-technical framework and draw parallels with HTTPS certification and Blu-Ray verification protocols. While acknowledging implementation challenges, we contend that hardware-based authentication offers a more tractable path forward, particularly from a policy perspective. As generative models approach perceptual indistinguishability, the research community should be wary of being overly optimistic with AI watermarking, and we argue that AI watermarking research efforts are better spent in the text and LLM space, which are ultimately not traceable to a physical sensor.

Given the growing use of generative artificial intelligence as a tool for creating multilingual content and bypassing both machine and traditional translation methods, this study explores the ability of linguistically trained individuals to discern machine-generated output from human-written text (HT). After brief training sessions on the textual anomalies typically found in synthetic text (ST), twenty-three postgraduate translation students analysed excerpts of Italian prose and assigned likelihood scores to indicate whether they believed they were human-written or AI-generated (ChatGPT-4o). The results show that, on average, the students struggled to distinguish between HT and ST, with only two participants achieving notable accuracy. Closer analysis revealed that the students often identified the same textual anomalies in both HT and ST, although features such as low burstiness and self-contradiction were more frequently associated with ST. These findings suggest the need for improvements in the preparatory training. Moreover, the study raises questions about the necessity of editing synthetic text to make it sound more human-like and recommends further research to determine whether AI-generated text is already sufficiently natural-sounding not to require further refinement.

Apple released its latest'budget' phone, the 599 iPhone 16e, back in February after months of feverish anticipation. But not to be outdone, rival tech giant Google has released its own handset at an'unbeatable' price – the Pixel 9a. The device – which at 499 is 100 cheaper than Apple's equivalent – has a 6.3-inch display, two rear cameras and more than 30 hours of battery life on a single charge. It's packed with'helpful' AI tools such as Gemini – Google's chatbot which was built to rival OpenAI's ChatGPT, now on Apple phones. MailOnline tests the new Google handset, described as a more accessible alternative to the firm's flagship Pixel 9 ( 799).

Earlier this week via blog post, OpenAI released their newest AI models: o3 and o4-mini. These models are the company's "smartest and most capable models to date" and their first reasoning models that can also reason when it comes to images. In short, these AI models can use an image--such as a photograph or a sketch--as part of an analysis. The models can also adjust, zoom in on, and rotate an image during reasoning. For the first time, our reasoning models can agentically use and combine every tool within ChatGPT, including web search, Python, image analysis, file interpretation, and image generation.

Fox News Flash top entertainment and celebrity headlines are here. James Cameron's stance on artificial intelligence has evolved over the past few years, and he feels Hollywood needs to embrace it in a few different ways. Cameron joined the board of directors for Stability AI last year, explaining his decision on the "Boz to the Future" podcast last week. "The goal was to understand the space, to understand what's on the minds of the developers," he said. How much resources you have to throw at it to create a new model that does a purpose-built thing, and my goal was to try to integrate it into a VFX workflow." He continued by saying the shift to AI is a necessary one. James Cameron wants Hollywood to implement AI more for big-budget films. WHAT IS ARTIFICIAL INTELLIGENCE (AI)? If we want to continue to see the kinds of movies that I've always loved and that I like to make and that I will go to see – 'Dune,' 'Dune: Part Two' or one of my films or big effects-heavy, CG-heavy films – we've got to figure out how to cut the cost of that in half. That's about doubling their speed to completion on a given shot, so your cadence is faster and your throughput cycle is faster, and artists get to move on and do other cool things and then other cool things, right? Cameron doesn't think films are ultimately "a big target" for companies like OpenAI. "Their goal is not to make GenAI movies.

Text-to-image models based on diffusion processes, such as DALL-E, Stable Diffusion, and Midjourney, are capable of transforming texts into detailed images and have widespread applications in art and design. As such, amateur users can easily imitate professional-level paintings by collecting an artist's work and fine-tuning the model, leading to concerns about artworks' copyright infringement. To tackle these issues, previous studies either add visually imperceptible perturbation to the artwork to change its underlying styles (perturbation-based methods) or embed post-training detectable watermarks in the artwork (watermark-based methods). However, when the artwork or the model has been published online, i.e., modification to the original artwork or model retraining is not feasible, these strategies might not be viable. To this end, we propose a novel method for data-use auditing in the text-to-image generation model. The general idea of ArtistAuditor is to identify if a suspicious model has been finetuned using the artworks of specific artists by analyzing the features related to the style. Concretely, ArtistAuditor employs a style extractor to obtain the multi-granularity style representations and treats artworks as samplings of an artist's style. Then, ArtistAuditor queries a trained discriminator to gain the auditing decisions. The experimental results on six combinations of models and datasets show that ArtistAuditor can achieve high AUC values (> 0.937). By studying ArtistAuditor's transferability and core modules, we provide valuable insights into the practical implementation. Finally, we demonstrate the effectiveness of ArtistAuditor in real-world cases by an online platform Scenario. ArtistAuditor is open-sourced at https://github.com/Jozenn/ArtistAuditor.