ChatGPT continues to get more and more capable as features such as web search and scheduled tasks are added, and the latest new AI tool pushed out by OpenAI is an advanced searching feature called Deep Research--yes, just like the similar tool inside Google Gemini. As you can guess from the name, the tool is designed to do a thorough search on the web for information related to your query, then present a detailed report to your specifications. According to OpenAI, Deep Research "leverages reasoning to search, interpret, and analyze massive amounts of text, images, and PDFs on the internet, pivoting as needed in reaction to information it encounters". Right now, you need to be a paying ChatGPT user to access Deep Research, so you'll have to give OpenAI at least 20 per month to make use of it. To date, there's been no word on if or when the feature will make its way to users on the free ChatGPT tier.

The early 2020s has seen the rise of two strange and potentially quite impactful social phenomena, namely pseudolaw, where users rely upon pseudolegal arguments that mimic the form and ritual of legal argumentation but fundamentally distort the content of law, and generative AI/LLMs, which generate content that uses probabilistic calculations to create outputs that look like human generated text. This article argues that the juxtaposition of the two phenomena helps to reveal that they both share two fundamental traits as both elevate form and appearance over substance and content, and users of both routinely mistake the form for the substance. In drawing upon legal theory, computer science, linguistics and cognitive psychology, the article argues that both phenomena rely upon creating illusions of meaning that users mistake for the underlying primary phenomenon. I then explore four implications of this conception of both phenomena. Firstly, both rely on human tendencies of conceptual pareidolia resulting in the erroneous perception of meaningful linguistic legal patterns from nebulous inputs. Secondly, both rely upon the confidence heuristic, the human cognitive bias for treating confidence as a proxy for competence. Thirdly, both succeed when the primary concern is with the form of the output and not its content. Fourthly, both rely heavily upon the magical thinking of users and the desire for the promise of the approach to be real. The article argues that the legal context helps to reveal a solution for the problems caused by both phenomena as it is only where users possess sufficient legal and technological literacy that it becomes possible to reveal to them the illusionary nature of the phenomena.

Collaboration can amalgamate diverse ideas, styles, and visual elements, fostering creativity and innovation among different designers. In collaborative design, sketches play a pivotal role as a means of expressing design creativity. However, designers often tend to not openly share these meticulously crafted sketches. This phenomenon of data island in the design area hinders its digital transformation under the third wave of AI. In this paper, we introduce a Federated Generative Artificial Intelligence Clothing system, namely FedGAI, employing federated learning to aid in sketch design. FedGAI is committed to establishing an ecosystem wherein designers can exchange sketch styles among themselves. Through FedGAI, designers can generate sketches that incorporate various designers' styles from their peers, drawing inspiration from collaboration without the need for data disclosure or upload. Extensive performance evaluations indicate that our FedGAI system can produce multi-styled sketches of comparable quality to human-designed ones while significantly enhancing efficiency compared to hand-drawn sketches.

Causal reasoning and compositional reasoning are two core aspirations in generative AI. Measuring the extent of these behaviors requires principled evaluation methods. We explore a unified perspective that considers both behaviors simultaneously, termed compositional causal reasoning (CCR): the ability to infer how causal measures compose and, equivalently, how causal quantities propagate through graphs. We instantiate a framework for the systematic evaluation of CCR for the average treatment effect and the probability of necessity and sufficiency. As proof of concept, we demonstrate the design of CCR tasks for language models in the LLama, Phi, and GPT families. On a math word problem, our framework revealed a range of taxonomically distinct error patterns. Additionally, CCR errors increased with the complexity of causal paths for all models except o1.

This research addresses the growing need to measure and understand AI literacy in the context of generative AI technologies. Through three sequential studies involving a total of 517 participants, we establish AI literacy as a coherent, measurable construct with significant implications for education, workforce development, and social equity. Study 1 (N=85) revealed a dominant latent factor - termed the "A-factor" - that accounts for 44.16% of variance across diverse AI interaction tasks. Study 2 (N=286) refined the measurement tool by examining four key dimensions of AI literacy: communication effectiveness, creative idea generation, content evaluation, and step-by-step collaboration, resulting in an 18-item assessment battery. Study 3 (N=146) validated this instrument in a controlled laboratory setting, demonstrating its predictive validity for real-world task performance. Results indicate that AI literacy significantly predicts performance on complex, language-based creative tasks but shows domain specificity in its predictive power. Additionally, regression analyses identified several significant predictors of AI literacy, including cognitive abilities (IQ), educational background, prior AI experience, and training history. The multidimensional nature of AI literacy and its distinct factor structure provide evidence that effective human-AI collaboration requires a combination of general and specialized abilities. These findings contribute to theoretical frameworks of human-AI collaboration while offering practical guidance for developing targeted educational interventions to promote equitable access to the benefits of generative AI technologies.

Image generative models, particularly diffusion-based models, have surged in popularity due to their remarkable ability to synthesize highly realistic images. However, since these models are data-driven, they inherit biases from the training datasets, frequently leading to disproportionate group representations that exacerbate societal inequities. Traditionally, efforts to debiase these models have relied on predefined sensitive attributes, classifiers trained on such attributes, or large language models to steer outputs toward fairness. However, these approaches face notable drawbacks: predefined attributes do not adequately capture complex and continuous variations among groups. To address these issues, we introduce the Debiasing Diffusion Model (DDM), which leverages an indicator to learn latent representations during training, promoting fairness through balanced representations without requiring predefined sensitive attributes. This approach not only demonstrates its effectiveness in scenarios previously addressed by conventional techniques but also enhances fairness without relying on predefined sensitive attributes as conditions. In this paper, we discuss the limitations of prior bias mitigation techniques in diffusion-based models, elaborate on the architecture of the DDM, and validate the effectiveness of our approach through experiments.

In their survey of authoring tools for computational narrative, Kybartas and Bidarra note that "we believe that creating a standard model of computational narrative could allow different systems to interact with the same narrative, without being restricted by incompatible models and definitions. Furthermore, such a model would also facilitate research into the generation of specific story components, e.g., allowing for multiple generators and even authors to collaborate on a given narrative" [Kybartas and Bidarra [2017]]. This paper proposes such a standard: the Universal Narrative Model (UNM). We foresee that generative AI will enable a new paradigm of storytelling technologies and processes: from assisting a writer of linear media (novels, film, television, etc.) by allowing them to test out scenes and characters before committing them to a script, all the way through to real-time storytelling systems in videogames which respond to a player's agency, and countless use cases in between [Peng et al. [2024]]. The UNM is designed to service any use case in which coherent narrative structure is a consideration, and in which authorial intent and direction is privileged. In the last five years, a robust body of research has demonstrated a wide variety of potential uses for computational narrative systems powered by generative AI, and some limited commercial deployments already exist [Yang et al. [2024], Hu et al. [2024]]. With such promise, however, comes a series of challenges: technical, narrative, and ethical. The goal of the Entertainment Technology Center's "Universal Narrative Model" project was to produce the UNM as an open standard. The ultimate directive of the project was to privilege, above all else, author-centric design and functionality, setting the stage for generative workflows which extend an author's narrative intent and creativity, rather than eclipse or replace it.

Fine-tuning based concept erasing has demonstrated promising results in preventing generation of harmful contents from text-to-image diffusion models by removing target concepts while preserving remaining concepts. To maintain the generation capability of diffusion models after concept erasure, it is necessary to remove only the image region containing the target concept when it locally appears in an image, leaving other regions intact. However, prior arts often compromise fidelity of the other image regions in order to erase the localized target concept appearing in a specific area, thereby reducing the overall performance of image generation. To address these limitations, we first introduce a framework called localized concept erasure, which allows for the deletion of only the specific area containing the target concept in the image while preserving the other regions. As a solution for the localized concept erasure, we propose a training-free approach, dubbed Gated Low-rank adaptation for Concept Erasure (GLoCE), that injects a lightweight module into the diffusion model. GLoCE consists of low-rank matrices and a simple gate, determined only by several generation steps for concepts without training. By directly applying GLoCE to image embeddings and designing the gate to activate only for target concepts, GLoCE can selectively remove only the region of the target concepts, even when target and remaining concepts coexist within an image. Extensive experiments demonstrated GLoCE not only improves the image fidelity to text prompts after erasing the localized target concepts, but also outperforms prior arts in efficacy, specificity, and robustness by large margin and can be extended to mass concept erasure.

The MIR serves as a critical selection mechanism for medical graduates entering specialized training in Spain. A study is to be conducted on the ability of generative AI models to meet the challenges presented by MIR, with emphasis on clinical reasoning, image interpretation and epidemiological calculations. This research evaluates LLM performance in complex clinical scenarios and explores the extent to which LLMs demonstrate medical reasoning beyond mere information recall. Findings The results reveal key insights into the performance of 22 LLMs on the MIR 2024 and 2025 exams. The exam features 210 multiple-choice questions covering diverse medical domains and incorporates case-based scenarios, image interpretation (25 questions), and laboratory data analysis.

A woman in Washington, D.C., views a manipulated video on January 24, 2019, that changes what is said by President Donald Trump and former President Barack Obama. This illustration photo taken on January 30, 2023 shows a phone screen displaying a statement from the head of security policy at META with a fake video of Ukrainian President Volodymyr Zelensky calling on his soldiers to lay down their weapons shown in the background, in Washington, D.C. (OLIVIER DOULIERY/AFP via Getty Images) NEW REALITY: Artificial intelligence (AI) is producing hyperrealistic "digital twins" of politicians, celebrities, pornographic material, and more – leaving victims of deepfake technology struggling to determine legal recourse. NO BOUNDARY: Scarlett Johansson has taken a vocal stand on artificial intelligence, after having her likeness and voice used without permission. Last year, Johansson said she had been asked to voice OpenAI's Chatbot by CEO Sam Altman, but turned down the job, only for people to notice that the feature, named "Sky," sounded almost exactly like the actress. It was like: If that can happen to me, how are we going to protect ourselves from this? There's no boundary here; we're setting ourselves up to be taken advantage of," the 40-year-old told InStyle Magazine earlier this month.