Image watermarks have been considered a promising technique to help detect AI-generated content, which can be used to protect copyright or prevent fake image abuse. In this work, we present a black-box method for removing invisible image watermarks, without the need of any dataset of watermarked images or any knowledge about the watermark system. Our approach is simple to implement: given a single watermarked image, we regress it by deep image prior (DIP). We show that from the intermediate steps of DIP one can reliably find an evasion image that can remove invisible watermarks while preserving high image quality. Due to its unique working mechanism and practical effectiveness, we advocate including DIP as a baseline invasion method for benchmarking the robustness of watermarking systems. Finally, by showing the limited ability of DIP and other existing black-box methods in evading training-based visible watermarks, we discuss the positive implications on the practical use of training-based visible watermarks to prevent misinformation abuse.

Current spectrum-sharing frameworks struggle with adaptability, often being either static or insufficiently dynamic. They primarily emphasize temporal sharing while overlooking spatial and spectral dimensions. We propose an adaptive, AI-driven spectrum-sharing framework within the O-RAN architecture, integrating discriminative and generative AI (GenAI) to forecast spectrum needs across multiple timescales and spatial granularities. A marketplace model, managed by an authorized spectrum broker, enables operators to trade spectrum dynamically, balancing static assignments with real-time trading. GenAI enhances traffic prediction, spectrum estimation, and allocation, optimizing utilization while reducing costs. This modular, flexible approach fosters operator collaboration, maximizing efficiency and revenue. A key research challenge is refining allocation granularity and spatio-temporal dynamics beyond existing models.

A bill headed to Utah's Senate floor would require police to include disclaimers in any report written with help from artificial intelligence. Introduced by Sen. Stephanie Pitcher, SB180 comes nearly a year after multiple police agencies across the country began testing software like Axon's Draft One, prompting concerns from critics and privacy advocates. Draft One was announced by Axon in April 2024, kicking off a major new phase for the company best known for manufacturing tasers and a popular line of body cameras used by law enforcement. Axon built Draft One using Microsoft's Azure OpenAI platform, and is designed to auto-generate police reports using only an officer's body cam audio records. Once processed, Draft One then crafts "a draft narrative quickly," reportedly cutting down on police officer's paperwork by as much as an hour per day.

Generative AI search, one of MIT Technology Review's 10 Breakthrough Technologies of 2025, is ushering a new era of the internet. Hear from MIT Technology Review editor in chief Mat Honan and executive editor Niall Firth as they explore how AI will alter search.

Artificial intelligence models from OpenAI and Meta often resort to simplistic and sometimes racist stereotypes when prompted to portray people of certain demographic identities – a notable flaw at a time when some tech companies and academic researchers want to replace humans with AI chatbots for some tasks. Companies such as Meta have already tried boosting engagement on social media platforms like Facebook and Instagram by deploying AI chatbots that mimic human profiles and respond to people's posts.

Meta just announced its first-ever LlamaCon, a dev conference dedicated to generative AI. It's scheduled for April 29. The company titled the event after its family of generative AI models. Meta promises to "share the latest on our open source AI developments to help developers do what they do best: build amazing apps and products." Beyond that vague description, we don't know much.

Elon Musk's artificial intelligence startup xAI has introduced Grok-3, the latest iteration of its chatbot that integrates with X, formerly Twitter. Grok-3 debut comes at a critical moment in the AI arms race as Musk looks to compete with the Chinese AI firm DeepSeek, Microsoft-backed OpenAI and Google. Musk's bot has seen less widespread adoption than DeepSeek's namesake chatbot, which wowed the world weeks ago and caused panic in stock markets, as well as OpenAI's ChatGPT and Google's Gemini. Grok-3 is being rolled out immediately to Premium subscribers of X, the social media platform owned by Musk. The chatbot can generate texts and images without many of the common guardrails against sexually suggestive imagery, vulgarity or the reproduction of well-known people's likenesses. "Grok-3 across the board is in a league of its own," Musk said during a livestream alongside three xAI engineers late on Monday.

This was the second time Linklaters had run its LinksAI benchmark tests, with the original exercise taking place in October 2023. In the first run, OpenAI's GPT 2, 3 and 4 were tested alongside Google's Bard. The exam has now been expanded to include o1, from OpenAI, and Google's Gemini 2.0, which was also released at the end of 2024. It did not involve DeepSeek's R1 - the apparently low cost Chinese model which astonished the world last month - or any other non-US AI tool. The test involved posing the type of questions which would require advice from a "competent mid-level lawyer" with two years' experience.

Elon Musk's artificial intelligence startup, xAI, showed off the updated Grok-3 model, showcasing a version of the chatbot technology that the billionaire has said is the "smartest AI on Earth." Across math, science and coding benchmarks, Grok-3 beats Alphabet's Google Gemini, DeepSeek's V3 model, Anthropic's Claude and OpenAI's GPT-4o, the company said via a live stream on Monday. Grok-3 has "more than 10 times" the computing power of its predecessor and completed pretraining in early January, Musk said in a presentation alongside three of xAI's engineers. "We're continually improving the models every day, and literally within 24 hours, you'll see improvements," Musk said.

To retrieve and compare scientific data of simulations and experiments in materials science, data needs to be easily accessible and machine readable to qualify and quantify various materials science phenomena. However, a majority of information is encoded within scientific documents limiting the capability of finding suitable literature as well as material properties. This manuscript showcases an automated workflow, which unravels the encoded information from scientific literature to a machine readable data structure of texts, figures, tables, equations and meta-data, using natural language processing and language as well as vision transformer models to generate a machine-readable database. The machine-readable database can be enriched with local data, as e.g. The study shows that such an automated workflow accelerates information retrieval, proximate context detection and material property extraction from multi-modal input data exemplarily shown for the research field of microstructural analyses of face-centered cubic single crystals. Ultimately, a Retrieval-Augmented Generation (RAG) based Large Language Model (LLM) enables a fast and e fficient question answering chat bot. Introduction Understanding physical processes in materials and material microstructures is of fundamental importance in facilitating their use in engineering applications. However, analyzing the increasing amount of existing scientific knowledge and extracting the relevant information for a desired research project is a challenging task. Especially, combining information from experiments, simulations and theory is of great significance as different aspects are considered at each discipline that together, ultimately, form a holistic picture [1, 2, 3, 4]. Machine learning (ML) and artificial intelligence (AI) have been recently used as advanced computational tools to accelerate the physical understanding in materials science research [3, 5, 6, 4, 7]. Recent progress in these computational methods enabled AI-assisted models with the ability to extrapolate beyond their data basis and generate novel materials science approaches, called generative AI (genAI) [8, 9]. Applying genAI leads for example to a novel design of crystalline materials [10], of molecule properties [11] and of architected materials [12].