So Why Did It Make a Web Browser? The AI giant has lost its imagination. According to Sam Altman, your web browser is outdated. "AI represents a rare, once-a-decade opportunity to rethink what a browser can be," OpenAI's CEO said yesterday when announcing the company's latest product: ChatGPT Atlas. In this new AI-powered browser, ChatGPT becomes the central mechanism for surfing the internet.

People Who Say They're Experiencing AI Psychosis Beg the FTC for Help The Federal Trade Commission received 200 complaints mentioning ChatGPT between November 2022 and August 2025. Several attributed delusions, paranoia, and spiritual crises to the chatbot. On March 13, a woman from Salt Lake City, Utah called the Federal Trade Commission to file a complaint against OpenAI's ChatGPT. She claimed to be acting "on behalf of her son, who was experiencing a delusional breakdown." "The consumer's son has been interacting with an AI chatbot called ChatGPT, which is advising him not to take his prescribed medication and telling him that his parents are dangerous," reads the FTC's summary of the call.

Nyalleng Moorosi is part of a movement aimed at involving more African voices in AI policymaking. The room is draped in white curtains, and a giant screen blinks with videos created with generative AI. A classic East African folk song by the Tanzanian singer Saida Karoli plays loudly on the speakers. Friends greet each other as waiters serve arrowroot crisps and sugary mocktails. A man and a woman wearing leopard skins atop their clothes sip beer and chat; many women are in handwoven Ethiopian garb with red, yellow, and green embroidery. "The best thing about the Indaba is always the parties," computer scientist Nyalleng Moorosi tells me.

SoftBank has raised at least $24 billion in loans and bonds so far in 2025 and is seeking to raise more in the overseas bond market. SoftBank Group is returning to the overseas bond market for the second time this year amid an aggressive fundraising push for artificial intelligence, led by its bet on OpenAI. The Japanese technology investment giant is looking to raise about $1.5 billion to $2 billion in the dollar debt market, and about €500 million ($580 million) from euro-denominated notes, according to people familiar with the matter. A spokesperson for SoftBank said the bond deal size hasn't been finalized, declining to comment further. With a heavy emphasis on new AI investments, SoftBank's billionaire founder Masayoshi Son has pledged as much as $500 billion for a project known as Stargate and announced a planned $30 billion stake in OpenAI earlier this year.

ChatGPT-maker OpenAI has unveiled an artificial intelligence-powered web browser to challenge competitors like Google, which operates Chrome, the most popular browser in the world. ChatGPT Atlas does away with the address bar that is a key feature in search, with boss Sam Altman saying it was built around ChatGPT as the company made the new browser available on Tuesday on Apple's MacOS operating system. The arrival of Atlas comes as OpenAI seeks new ways to monetise its massive bet on artificial intelligence (AI) and capitalise on its growing user base. OpenAI said Atlas would also offer a paid agent mode that conducts searches on its own for users of its popular chatbot. The agent mode feature will be available only to paying ChatGPT subscribers.

Identity preserving editing of faces is a generative task that enables modifying the illumination, adding/removing eyeglasses, face aging, editing hairstyles, modifying expression etc., while preserving the identity of the face. Recent progress in 2D generative models have enabled photorealistic editing of faces using simple techniques leveraging the compositionality in GANs. However, identity preserving editing for 3D faces with a given set of attributes is a challenging task as the generative model must reason about view consistency from multiple poses and render a realistic 3D face. Further, 3D portrait editing requires large-scale attribute labelled datasets and presents a trade-off between editability in low-resolution and inflexibility to editing in high resolution. In this work, we aim to alleviate some of the constraints in editing 3D faces by identifying latent space directions that correspond to photorealistic edits. To address this, we present a method that builds on recent advancements in 3D-aware deep generative models and 2D portrait editing techniques to perform efficient few-shot identity preserving attribute editing for 3D-aware generative models. We aim to show from experimental results that using just ten or fewer labelled images of an attribute is sufficient to estimate edit directions in the latent space that correspond to 3D-aware attribute editing. In this work, we leverage an existing face dataset with masks to obtain the synthetic images for few attribute examples required for estimating the edit directions. Further, to demonstrate the linearity of edits, we investigate one-shot stylization by performing sequential editing and use the (2D) Attribute Style Manipulation (ASM) technique to investigate a continuous style manifold for 3D consistent identity preserving face aging. Code and results are available at: https://vishal-vinod.github.io/gmpi-edit/

Large Language Models (LLMs) deliver powerful reasoning and generation capabilities but incur substantial run-time costs when operating in agentic workflows that chain together lengthy prompts and process rich data streams. We introduce CompactPrompt, an end-to-end pipeline that merges hard prompt compression with lightweight file-level data compression. CompactPrompt first prunes low-information tokens from prompts using self-information scoring and dependency-based phrase grouping. In parallel, it applies n-gram abbreviation to recurrent textual patterns in attached documents and uniform quantization to numerical columns, yielding compact yet semantically faithful representations. Integrated into standard LLM agents, CompactPrompt reduces total token usage and inference cost by up to 60% on benchmark dataset like TAT-QA and FinQA, while preserving output quality (Results in less than 5% accuracy drop for Claude-3.5-Sonnet, and GPT-4.1-Mini) CompactPrompt helps visualize real-time compression decisions and quantify cost-performance trade-offs, laying the groundwork for leaner generative AI pipelines.

Preprint repositories become central infrastructures for scholarly communication. Their expansion transforms how research is circulated and evaluated before journal publication. Generative large language models (LLMs) introduce a further potential disruption by altering how manuscripts are written. While speculation abounds, systematic evidence of whether and how LLMs reshape scientific publishing remains limited. This paper addresses the gap through a large-scale analysis of more than 2.1 million preprints spanning 2016--2025 (115 months) across four major repositories (i.e., arXiv, bioRxiv, medRxiv, SocArXiv). We introduce a multi-level analytical framework that integrates interrupted time-series models, collaboration and productivity metrics, linguistic profiling, and topic modeling to assess changes in volume, authorship, style, and disciplinary orientation. Our findings reveal that LLMs have accelerated submission and revision cycles, modestly increased linguistic complexity, and disproportionately expanded AI-related topics, while computationally intensive fields benefit more than others. These results show that LLMs act less as universal disruptors than as selective catalysts, amplifying existing strengths and widening disciplinary divides. By documenting these dynamics, the paper provides the first empirical foundation for evaluating the influence of generative AI on academic publishing and highlights the need for governance frameworks that preserve trust, fairness, and accountability in an AI-enabled research ecosystem.

Rapid advancement in generative AI and large language models (LLMs) has enabled the generation of highly realistic and contextually relevant digital content. LLMs such as ChatGPT with DALL-E integration and Stable Diffusion techniques can produce images that are often indistinguishable from those created by humans, which poses challenges for digital content authentication. Verifying the integrity and origin of digital data to ensure it remains unaltered and genuine is crucial to maintaining trust and legality in digital media. In this paper, we propose an embedding-based AI image detection framework that utilizes image embeddings and a vector similarity to distinguish AI-generated images from real (human-created) ones. Our methodology is built on the hypothesis that AI-generated images demonstrate closer embedding proximity to other AI-generated content, while human-created images cluster similarly within their domain. To validate this hypothesis, we developed a system that processes a diverse dataset of AI and human-generated images through five benchmark embedding models. Extensive experimentation demonstrates the robustness of our approach, and our results confirm that moderate to high perturbations minimally impact the embedding signatures, with perturbed images maintaining close similarity matches to their original versions. Our solution provides a generalizable framework for AI-generated image detection that balances accuracy with computational efficiency.

Large Language Models (LLMs) have rapidly transitioned from academic research to core components of real-world applications, especially since the emergence of high-profile foundation models such as OpenAI's GPT series [17, 140], Google Gemini [9], Meta Llama [175, 176], Anthropic Claude [12], Alibaba Qwen [11, 210, 209], and Doubao [172]. Today, LLMs are deployed across an unprecedented range of sectors--from web search and code assistants to legal, educational, and healthcare domains--reaching hundreds of millions of end users globally. The rapid adoption of LLMs has ushered in a new era of AI-powered services, but it also brings serious safety and security risks. These risks manifest in multiple forms, ranging from misinformation and privacy leaks to adversarial attacks that exploit model vulnerabilities. In particular, a growing body of work shows that carefully crafted jailbreak prompts can bypass alignment constraints, inducing models to produce sensitive, illegal, or harmful content. Alarmingly, recent studies report that such attacks achieve success rates exceeding 90% even on flagship models such as GPT-4, Claude 3, and DeepSeek-R1 [124, 42, 154, 118]. The outputs generated through these attacks could be used for malicious purposes, underscoring the urgent need for close attention and mitigation.