The eighteen plus symbol (18+) appears on a smartphone screen, and the OpenAI logo displays as the background on a laptop screen in this photo illustration in Athens, Greece, on October 16, 2025. The eighteen plus symbol (18+) appears on a smartphone screen, and the OpenAI logo displays as the background on a laptop screen in this photo illustration in Athens, Greece, on October 16, 2025. In August, OpenAI CEO Sam Altman said on a podcast that he was "proud" that his company had not gotten "distracted" by putting features like a "sexbot avatar" into ChatGPT. But on Tuesday, he announced that adult users will be able to access explicit interactive experiences, marking a major shift in the company's practices. "In December, as we roll out age-gating more fully and as part of our'treat adult users like adults' principle, we will allow even more, like erotica for verified adults," Altman said in a post on X.

For patients with inflammatory bowel disease, antibiotics can be a double-edged sword. The broad-spectrum drugs often prescribed for gut flare-ups can kill helpful microbes alongside harmful ones, sometimes worsening symptoms over time. When fighting gut inflammation, you don't always want to bring a sledgehammer to a knife fight. Researchers at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) and McMaster University have identified a new compound that takes a more targeted approach. The molecule, called enterololin, suppresses a group of bacteria linked to Crohn's disease flare-ups while leaving the rest of the microbiome largely intact.

Tokyo police believe the man made about 20,000 sexually explicit images of 262 women, such as actors and idols, and amassed sales of ¥1.2 million between October last year and September this year. Tokyo police have arrested a 31-year-old man for allegedly creating fake sexual images of female celebrities with generative artificial intelligence technology and displaying them online, it was learned Thursday. It is the first time that police in Japan have cracked down on sexual deepfake images of celebrities created with generative AI. The suspect, Hiroya Yokoi of the city of Akita, has admitted he began making deepfakes to earn a small amount of money, which he used to cover living expenses and repay a student loan. Authorities believe Yokoi made a total of about 20,000 sexually explicit images of 262 women, such as actors, television personalities and idols, and amassed sales of ¥1.2 million between October last year and September this year.

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Inverse design optimization aims to infer system parameters from observed solutions, posing critical challenges across domains such as semiconductor manufacturing, structural engineering, materials science, and fluid dynamics. The lack of explicit mathematical representations in many systems complicates this process and makes the first order optimization impossible. Mainstream approaches, including generative AI and Bayesian optimization, address these challenges but have limitations. Generative AI is computationally expensive, while Bayesian optimization, relying on surrogate models, suffers from scalability, sensitivity to priors, and noise issues, often leading to suboptimal solutions. This paper introduces Deep Physics Prior (DPP), a novel method enabling first-order gradient-based inverse optimization with surrogate machine learning models. By leveraging pretrained auxiliary Neural Operators, DPP enforces prior distribution constraints to ensure robust and meaningful solutions. This approach is particularly effective when prior data and observation distributions are unknown.

Generative AI systems are increasingly assisting and acting on behalf of end users in practical settings, from digital shopping assistants to next-generation autonomous cars. In this context, safety is no longer about blocking harmful content, but about preempting downstream hazards like financial or physical harm. Yet, most AI guardrails continue to rely on output classification based on labeled datasets and human-specified criteria,making them brittle to new hazardous situations. Even when unsafe conditions are flagged, this detection offers no path to recovery: typically, the AI system simply refuses to act--which is not always a safe choice. In this work, we argue that agentic AI safety is fundamentally a sequential decision problem: harmful outcomes arise from the AI system's continually evolving interactions and their downstream consequences on the world. We formalize this through the lens of safety-critical control theory, but within the AI model's latent representation of the world. This enables us to build predictive guardrails that (i) monitor an AI system's outputs (actions) in real time and (ii) proactively correct risky outputs to safe ones, all in a model-agnostic manner so the same guardrail can be wrapped around any AI model. We also offer a practical training recipe for computing such guardrails at scale via safety-critical reinforcement learning. Our experiments in simulated driving and e-commerce settings demonstrate that control-theoretic guardrails can reliably steer LLM agents clear of catastrophic outcomes (from collisions to bankruptcy) while preserving task performance, offering a principled dynamic alternative to today's flag-and-block guardrails.

Artificial Intelligence (AI) and machine learning (ML) models have made stark advances in the past three years, fueled by the development of foundation models (FM) trained on large-scale multimodal data. Following the public release of several successful FMs (OpenAI (2022); Brown et al. (2020); Bommasani et al. (2022)), FMs such as large language models (LLMs) and vision language models (VLMs) have bridged vision, language, and other modalities. In many Computer Science subfields such as Natural Language Processing (NLP) and Computer Vision (CV), FMs have demonstrated strong compositional performance and generalization capabilities (Awais et al. (2025); Gunter et al. (2024)), emerging as widely-used tools (Bommasani et al. (2022)) that provide a flexible backbones for innovation in other fields (Moor et al. (2023); Sartor & Thompson (2025); Firoozi et al. (2024)). Conducting FM research requires significant data, computing, and human resources (Cottier et al. (2024); Maslej et al. (2024); Crawford (2024)). A central concern in the field is whether greater access to such resources directly translates into more impactful research outcomes (Acemoglu (2024); Dodge et al. (2019); OpenAI (2018)), such as more research publications, or higher citation counts (Sinclair et al. (2023); Anjum et al. (2019)). The answer to this question has important implications for how resources are allocated, which research directions are prioritized, and how equitable participation in FM research can be ensured. However, the cost of research is often difficult to quantify due to lack of uniform disclosure on resource distribution (Bommasani et al. (2024)). Absent widespread disclosure, funding is perhaps most easily characterized in the concrete cost of purchasing or renting hardware (e.g., computing clusters, or chips), through there are also software, cloud storage services, and specialized software platform costs.

Department of Physics, University of Central Florida, 4111 Libra Drive, Orlando, Florida, USA 32816 We present a method for generating large numbers of isomorphic physics problems using generative AI services such as ChatGPT, through prompt chaining and tool use. This approach enables precise control over structural variations --such as numeric values and spatial relations -while supporting diverse contextual variations in the problem body. By utilizing the Python code interpreter, the method supports automatic solution validation and simple diagram generation, addressing key limitations in existing LLM -based methods. We generated two example isomorphic problem banks and compared the outcome against two simpler prompt - based approaches. Results show that prompt-chaining produces significantly higher quality and more consistent outputs than simpler, non -chaining prompts. We also show that GenAI services can be used to validate the quality of the generated isomorphic problems. This work demonstrates a promising method for efficient and scalable problem creation accessible to the average instructor, which opens new possibilities for personalized adaptive testing and automated content development. I. INTRODUCTION There has been significant progress in developing Automated Question Generation (AQG) and Automated Item Generation (AIG) technologies in education over the past decade. These technologies aim to reduce the time and cost of item creation while increasing t he availability of questions for both assessment and practice [1] . Early AQG/AIG approaches primarily relied on hard-coded, template-based methods, which were often time - consuming to develop and required domain-specific programming [2] . More recent research has shifted toward leveraging large language models (LLMs).

The AI bubble is heading towards a burst but it won't be the end of AI Economists, bankers and even the boss of OpenAI are warning of a rapidly inflating AI bubble. If and when it bursts, what will happen to the technological breakthroughs of the past few years? The hundreds of billions of dollars being spent on AI seem to have inflated a global financial bubble that's now fit to burst, leaving companies and investors at risk of holding vast debt that cannot be serviced by the meagre revenue brought in by current AI services. But what does that mean for the future of the technology underpinning this financial feeding frenzy? In recent weeks, warnings of a potential AI bubble have come from the International Monetary Fund, the Bank of England, the head of the largest US bank, and even OpenAI boss Sam Altman .

Stop Trying To Make A.I. Trendy From vandalized subway ads to bespoke caps, A.I. startups are flooding traditional marketing spaces and getting backlash for it. Please enable javascript to get your Slate Plus feeds. If you can't access your feeds, please contact customer support. Check your phone for a link to finish setting up your feed. Please enter a valid phone number.