You can turn a cluster of Macs into an AI supercomputer in macOS Tahoe 26.2

X has updated its AI tool Grok to prevent the editing of images of real people into revealing clothing, such as bikinis, and has geoblocked this feature in regions where it is illegal. This decision follows significant backlash over the tool's potential to create nonconsensual sexualized imagery.

Recent advancements in artificial intelligence (AI) and high-throughput testing have unveiled the stability limits of organic redox flow batteries, showcasing the potential of these technologies to enhance scientific research and innovation.

AI models are increasingly proficient at identifying software vulnerabilities, prompting experts to suggest that the tech industry must reconsider its software development practices. This advancement indicates a significant shift in the capabilities of AI technologies, particularly in cybersecurity.

Google has announced that its AI model, Gemini, can now pull context from other Google applications, enhancing its functionality and user experience. This capability allows Gemini to provide more personalized and relevant responses by integrating data from services like Gmail and Calendar, contingent on user consent.

The UK government has announced the removal of the digital ID requirement for workers, a decision that reflects ongoing debates about privacy and technology in the workplace. This change is part of a broader reassessment of digital identification systems in the country.

A recent study published on arXiv investigates the generalization capabilities of AI-generated text detectors, revealing that while these detectors perform well on in-domain benchmarks, they often fail to generalize across various generation conditions, such as unseen prompts and different model families. The research employs a comprehensive benchmark involving multiple prompting strategies and large language models to analyze performance variance through linguistic features.

A recent study has introduced a principled design for interpretable automated scoring systems aimed at large-scale educational assessments, addressing the growing demand for transparency in AI-driven evaluations. The proposed framework, AnalyticScore, emphasizes four principles of interpretability: Faithfulness, Groundedness, Traceability, and Interchangeability (FGTI).

A recent study introduces RAVEN, a novel approach to erasing invisible watermarks from AI-generated images by reformulating watermark removal as a view synthesis problem. This method generates alternative views of the same content, effectively removing watermarks while maintaining visual fidelity.