Facebook is no stranger to moderating and mitigating misinformation on its platform, having long employed machine learning and artificial intelligence systems to help supplement its human-led moderation efforts. At the start of October, the company extended its machine learning expertise to its advertising efforts with an experimental set of generative AI tools that can perform tasks like generating backgrounds, adjusting image and creating captions for an advertiser's video content. Reuters reports Monday that Meta will specifically not make those tools available to political marketers ahead of what is expected to be a brutal and divisive national election cycle. Meta's decision to bar the use of generative AI is in line with much of the social media ecosystem, though, as Reuters is quick to point out, the company, "has not yet publicly disclosed the decision in any updates to its advertising standards." TikTok and Snap both ban political ads on their networks, Google employs a "keyword blacklist" to prevent its generative AI advertising tools from straying into political speech and X (formerly Twitter) is, well, you've seen it.

Human-AI interaction in text production increases complexity in authorship. In two empirical studies (n1 = 30 & n2 = 96), we investigate authorship and ownership in human-AI collaboration for personalized language generation. We show an AI Ghostwriter Effect: Users do not consider themselves the owners and authors of AI-generated text but refrain from publicly declaring AI authorship. Personalization of AI-generated texts did not impact the AI Ghostwriter Effect, and higher levels of participants' influence on texts increased their sense of ownership. Participants were more likely to attribute ownership to supposedly human ghostwriters than AI ghostwriters, resulting in a higher ownership-authorship discrepancy for human ghostwriters. Rationalizations for authorship in AI ghostwriters and human ghostwriters were similar. We discuss how our findings relate to psychological ownership and human-AI interaction to lay the foundations for adapting authorship frameworks and user interfaces in AI in text-generation tasks.

The past decade has been transformative for mental health research and practice. The ability to harness large repositories of data, whether from electronic health records (EHR), mobile devices, or social media, has revealed a potential for valuable insights into patient experiences, promising early, proactive interventions, as well as personalized treatment plans. Recent developments in generative artificial intelligence, particularly large language models (LLMs), show promise in leading digital mental health to uncharted territory. Patients are arriving at doctors' appointments with information sourced from chatbots, state-of-the-art LLMs are being incorporated in medical software and EHR systems, and chatbots from an ever-increasing number of startups promise to serve as AI companions, friends, and partners. This article presents contemporary perspectives on the opportunities and risks posed by LLMs in the design, development, and implementation of digital mental health tools. We adopt an ecological framework and draw on the affordances offered by LLMs to discuss four application areas -- care-seeking behaviors from individuals in need of care, community care provision, institutional and medical care provision, and larger care ecologies at the societal level. We engage in a thoughtful consideration of whether and how LLM-based technologies could or should be employed for enhancing mental health. The benefits and harms our article surfaces could serve to help shape future research, advocacy, and regulatory efforts focused on creating more responsible, user-friendly, equitable, and secure LLM-based tools for mental health treatment and intervention.

Large Multimodal Models (LMMs) have demonstrated impressive performance across various vision and language tasks, yet their potential applications in recommendation tasks with visual assistance remain unexplored. To bridge this gap, we present a preliminary case study investigating the recommendation capabilities of GPT-4V(ison), a recently released LMM by OpenAI. We construct a series of qualitative test samples spanning multiple domains and employ these samples to assess the quality of GPT-4V's responses within recommendation scenarios. Evaluation results on these test samples prove that GPT-4V has remarkable zero-shot recommendation abilities across diverse domains, thanks to its robust visual-text comprehension capabilities and extensive general knowledge. However, we have also identified some limitations in using GPT-4V for recommendations, including a tendency to provide similar responses when given similar inputs. This report concludes with an in-depth discussion of the challenges and research opportunities associated with utilizing GPT-4V in recommendation scenarios. Our objective is to explore the potential of extending LMMs from vision and language tasks to recommendation tasks. We hope to inspire further research into next-generation multimodal generative recommendation models, which can enhance user experiences by offering greater diversity and interactivity.

Intelligent structural design using AI can effectively reduce time overhead and increase efficiency. It has potential to become the new design paradigm in the future to assist and even replace engineers, and so it has become a research hotspot in the academic community. However, current methods have some limitations to be addressed, whether in terms of application scope, visual quality of generated results, or evaluation metrics of results. This study proposes a comprehensive solution. Firstly, we introduce building information modeling (BIM) into intelligent structural design and establishes a structural design pipeline integrating BIM and generative AI, which is a powerful supplement to the previous frameworks that only considered CAD drawings. In order to improve the perceptual quality and details of generations, this study makes 3 contributions. Firstly, in terms of generation framework, inspired by the process of human drawing, a novel 2-stage generation framework is proposed to replace the traditional end-to-end framework to reduce the generation difficulty for AI models. Secondly, in terms of generative AI tools adopted, diffusion models (DMs) are introduced to replace widely used generative adversarial network (GAN)-based models, and a novel physics-based conditional diffusion model (PCDM) is proposed to consider different design prerequisites. Thirdly, in terms of neural networks, an attention block (AB) consisting of a self-attention block (SAB) and a parallel cross-attention block (PCAB) is designed to facilitate cross-domain data fusion. The quantitative and qualitative results demonstrate the powerful generation and representation capabilities of PCDM. Necessary ablation studies are conducted to examine the validity of the methods. This study also shows that DMs have the potential to replace GANs and become the new benchmark for generative problems in civil engineering.

There is no easy way to explain the sum of Google's knowledge. A growing web of hundreds of billions of websites, more data than even 100,000 of the most expensive iPhones mashed together could possibly store. But right now, I can say this: Google is confused about whether there's an African country beginning with the letter k. I've asked the search engine to name it. "What is an African country beginning with K?" In response, the site has produced a "featured snippet" answer--one of those chunks of text that you can read directly on the results page, without navigating to another website.

Facebook owner Meta is barring political advertisers from using its new generative AI advertising products, a company spokesperson said Monday, cutting off campaigns' access to tools lawmakers have warned could turbocharge the spread of election misinformation. Meta has not yet publicly disclosed the decision in any updates to its advertising standards, which prohibit ads with content that has been debunked by the company's fact-checking partners but do not appear to have any rules specifically on AI. The policy comes a month after Meta -- the world's second-biggest platform for digital ads -- announced it was starting to expand advertisers' access to AI-powered advertising tools that can instantly create backgrounds, image adjustments and variations of ad copy in response to simple text prompts.

The newest model is capable of accepting much longer inputs than previous versions -- up to 300 pages of text, compared to the current limit of 50. This means that theoretically, prompts can be a lot longer and more complex, and responses might be more meaningful. OpenAI has also updated the data that GPT-4 Turbo is trained on. The company claims that the newest model now has knowledge about the world until April 2023. The previous version was only caught up until September 2021, although recent updates to the non-Turbo GPT-4 did include the ability to browse the internet to get the latest information. GPT-4 Turbo will also accept images as prompts directly in the chat box, wherein it can generate captions or provide a description of what the image depicts.

Users of the free version of ChatGPT or ChatGPT were not included. OpenAI is not the first to offer such legal protection, though as the creator of the wildly popular ChatGPT, which Altman said has 100 million weekly users, it is a heavyweight player in the industry. Google, Microsoft and Amazon have made similar offers to users of their generative AI software. Getty Images, Shutterstock and Adobe have extended similar financial liability protection for their image-making software. Altman made the announcement at OpenAI's first ever developer conference, meant to attract programmers working with ChatGPT.

It's been nearly a year since ChatGPT's public debut and its evolution since then has been nothing short of extraordinary. In just over 11 months, OpenAI's chatbot has gained the ability to write programming code, process information between multiple modalities and expand its reach across the internet with APIs. During OpenAI's 2023 Dev Day keynote address Monday, CEO Sam Altman and other executives took to the stage in San Francisco to unveil the chatbot's latest iteration, ChatGPT-4 Turbo, as well as an exciting new way to bring generative AI technology to everybody, regardless of their coding capability: GPTs! GPTs are small, task-specific iterations of ChatGPT. Think of them like the single-purpose apps and features on your phone but instead of them maintaining a timer or stop watch, or a digital assistant transcribing your voice instructions into a shopping list, GPTs will do, basically anything you train them to. OpenAI offers up eight examples of what GPT's can be used for, anything from a digital kitchen assistant that suggests recipes based on whats in your pantry to a math mentor to help your kids through their homework to a Sticker Wiz that will, "turn your wildest dreams into die-cut stickers, shipped right to your door."