The frenzy to create Ghibli-style AI art using ChatGPT's image-generation tool led to a record surge in users for OpenAI's chatbot last week, straining its servers and temporarily limiting the feature's usage. The viral trend saw users from across the globe flood social media with images based on the hand-drawn style of the famed Japanese animation outfit, Studio Ghibli, founded by renowned director Hayao Miyazaki and known for movies such as "Spirited Away" and "My Neighbor Totoro." Average weekly active users breached the 150 million mark for the first time this year, according to data from market research firm Similarweb.

ChatGPT's built-in image generation feature is now available to everyone. OpenAI CEO Sam Altman said last week that the company is delaying its rollout to free tier "for a while," because the tool was way more popular than they had expected. But the company made the feature available to free users over the weekend, allowing them to generate images from within ChatGPT and without having to switch to OpenAI's DALL-E generator. Prior to its rollout to the free tier, the tool was only available to Plus, Pro and Team subscribers. Altman previously said that free users will get a limit of three images per day.

OpenAI on Monday said it raised 40 billion in a new funding round that valued the ChatGPT maker at 300 billion, the biggest capital-raising session ever for a startup. The infusion of cash comes in a partnership with Japanese investment giant SoftBank Group and "enables us to push the frontiers of AI research even further," the San Francisco-based company said in a post on its website. "Their support will help us continue building AI systems that drive scientific discovery, enable personalized education, enhance human creativity, and pave the way toward AGI (artificial general intelligence) that benefits all of humanity," the company said.

OpenAI said it had raised US 40bn in a funding round that valued the ChatGPT maker at 300bn – the biggest capital-raising session ever for a startup. It comes in a partnership with the Japanese investment group SoftBank and "enables us to push the frontiers of AI research even further," OpenAI announced, adding it would "pave the way toward AGI (artificial general intelligence)" for which "massive computing power is essential". SoftBank said it wanted to realise "artificial super intelligence" (ASI) surpassing human intelligence and OpenAI was the partner closest to achieving that goal. SoftBank is to put 10bn at first into OpenAI and 30bn more by the end of 2025 if certain conditions are met. Also on Monday, OpenAI announced it was building a more open generative AI model as it faces growing competition in the open-source space from DeepSeek and Meta.

Generative networks have shown remarkable success in learning complex data distributions, particularly in generating high-dimensional data from lower-dimensional inputs. While this capability is well-documented empirically, its theoretical underpinning remains unclear. One common theoretical explanation appeals to the widely accepted manifold hypothesis, which suggests that many real-world datasets, such as images and signals, often possess intrinsic low-dimensional geometric structures. Under this manifold hypothesis, it is widely believed that to approximate a distribution on a $d$-dimensional Riemannian manifold, the latent dimension needs to be at least $d$ or $d+1$. In this work, we show that this requirement on the latent dimension is not necessary by demonstrating that generative networks can approximate distributions on $d$-dimensional Riemannian manifolds from inputs of any arbitrary dimension, even lower than $d$, taking inspiration from the concept of space-filling curves. This approach, in turn, leads to a super-exponential complexity bound of the deep neural networks through expanded neurons. Our findings thus challenge the conventional belief on the relationship between input dimensionality and the ability of generative networks to model data distributions. This novel insight not only corroborates the practical effectiveness of generative networks in handling complex data structures, but also underscores a critical trade-off between approximation error, dimensionality, and model complexity.

Generative AI has the potential to transform personalization and accessibility of education. However, it raises serious concerns about accuracy and helping students become independent critical thinkers. In this study, we designed a helpful AI "Peer" to help students correct fundamental physics misconceptions related to Newtonian mechanic concepts. In contrast to approaches that seek near-perfect accuracy to create an authoritative AI tutor or teacher, we directly inform students that this AI can answer up to 40% of questions incorrectly. In a randomized controlled trial with 165 students, those who engaged in targeted dialogue with the AI Peer achieved post-test scores that were, on average, 10.5 percentage points higher--with over 20 percentage points higher normalized gain--than a control group that discussed physics history. Qualitative feedback indicated that 91% of the treatment group's AI interactions were rated as helpful. Furthermore, by comparing student performance on pre-and post-test questions about the same concept, along with experts' annotations of the AI interactions, we find initial evidence suggesting the improvement in performance does not depend on the correctness of the AI. With further research, the AI Peer paradigm described here could open new possibilities for how we learn, adapt to, and grow with AI. Students have recently been exposed to the remarkable capabilities of Generative AI (AI) in education (AIED). For example, OpenAI's ChatGPT has been reported to successfully support teaching preparation, assessment design and grading, and student learning (Lo, 2023). Systems like ChatGPT show potential to save time and enhance teaching and learning, including critical and higher-order thinking tasks (Lo, 2023).

We propose a numerical method of searching for parameters with experimental constraints in generic flavor models by utilizing diffusion models, which are classified as a type of generative artificial intelligence (generative AI). As a specific example, we consider the $S_4^\prime$ modular flavor model and construct a neural network that reproduces quark masses, the CKM matrix, and the Jarlskog invariant by treating free parameters in the flavor model as generating targets. By generating new parameters with the trained network, we find various phenomenologically interesting parameter regions where an analytical evaluation of the $S_4^\prime$ model is challenging. Additionally, we confirm that the spontaneous CP violation occurs in the $S_4^\prime$ model. The diffusion model enables an inverse problem approach, allowing the machine to provide a series of plausible model parameters from given experimental data. Moreover, it can serve as a versatile analytical tool for extracting new physical predictions from flavor models.

Increased usage of generative AI (GenAI) in Human-Computer Interaction (HCI) research induces a climate impact from carbon emissions due to energy consumption of the hardware used to develop and run GenAI models and systems. The exact energy usage and and subsequent carbon emissions are difficult to estimate in HCI research because HCI researchers most often use cloud-based services where the hardware and its energy consumption are hidden from plain view. The HCI GenAI CO2ST Calculator is a tool designed specifically for the HCI research pipeline, to help researchers estimate the energy consumption and carbon footprint of using generative AI in their research, either a priori (allowing for mitigation strategies or experimental redesign) or post hoc (allowing for transparent documentation of carbon footprint in written reports of the research).

LLMs increasingly serve as tools for knowledge acquisition, yet users cannot effectively specify how they want information presented. When users request that LLMs "cite reputable sources," "express appropriate uncertainty," or "include multiple perspectives," they discover that current interfaces provide no structured way to articulate these preferences. The result is prompt sharing folklore: community-specific copied prompts passed through trust relationships rather than based on measured efficacy. We propose the Epistemic Alignment Framework, a set of ten challenges in knowledge transmission derived from the philosophical literature of epistemology, concerning issues such as evidence quality assessment and calibration of testimonial reliance. The framework serves as a structured intermediary between user needs and system capabilities, creating a common vocabulary to bridge the gap between what users want and what systems deliver. Through a thematic analysis of custom prompts and personalization strategies shared on online communities where these issues are actively discussed, we find users develop elaborate workarounds to address each of the challenges. We then apply our framework to two prominent model providers, OpenAI and Anthropic, through content analysis of their documented policies and product features. Our analysis shows that while these providers have partially addressed the challenges we identified, they fail to establish adequate mechanisms for specifying epistemic preferences, lack transparency about how preferences are implemented, and offer no verification tools to confirm whether preferences were followed. For AI developers, the Epistemic Alignment Framework offers concrete guidance for supporting diverse approaches to knowledge; for users, it works toward information delivery that aligns with their specific needs rather than defaulting to one-size-fits-all approaches.

--Artificial intelligence (AI) trends vary significantly across global regions, shaping the trajectory of innovation, regulation, and societal impact. This variation influences how dif - ferent regions approach AI development, balancing technological progress with ethical and regulatory considerations. This study conducts a comparative analysis of AI trends in the United States (US), the European Union (EU), and Asia, focusing on three key dimensions: generative AI, ethical oversight, and industrial applications. The US prioritizes market -driven innovation with minimal regulatory constraints, the EU enforces a precautionary risk -based framework emphasizing ethical safeguards, and Asia employs state -guided AI strategies that balance rapid deployment with regulatory oversight. Although these approaches reflect different economic models and policy priorities, their divergence poses challenges to international collaboration, regulatory harmonization, and the development of global AI standards. To address these challenges, this paper synthesizes regional strengths to propose an adaptive AI governance framework that integrates risk -tiered oversight, innovation accelerators, and strategic alignment mechanisms. By bridging governance gaps, this study offers actionable insights for fostering responsible AI development while ensuring a balance between technological progress, ethical imperatives, and regulatory coherence. Artificial intelligence (AI) has emerged as a transformative force in the 21st century, reshaping industries, governance structures, and societal interactions at an unprecedented pace. From generative AI creating human - like text and images to autonomous systems revolutionizing healthcare, finance, and manufacturing, AI's influence is profound and far - reaching.