Can’t tech a joke: AI does not understand puns, study finds

UK Prime Minister Keir Starmer announced that the social media platform X has indicated to government officials that it is taking steps to comply with UK laws by restricting the generation of non-consensual sexual images. This response follows significant public backlash regarding the platform's AI tool, Grok, which has faced criticism for its controversial outputs.

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.

Following significant public backlash, Elon Musk's social media platform X has communicated to the UK government that it is taking steps to comply with local laws regarding the use of its AI tool, Grok, which has been criticized for generating nonconsensual sexualized images of women and children. Recent polling indicates that 58% of Britons believe X should be banned if it does not address these issues effectively.

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.

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.

Experts warn that the use of AI to create harmful sexualized imagery, particularly targeting women and children, is just beginning, as evidenced by the controversial Grok AI chatbot developed by Elon Musk's xAI. Despite recent attempts to implement safeguards, users continue to exploit the tool for generating explicit content.

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 titled 'Calibration Is Not Enough: Evaluating Confidence Estimation Under Language Variations' highlights the limitations of current confidence estimation methods for large language models (LLMs), emphasizing the need for evaluations that account for language variations and semantic differences. The research proposes a new framework that assesses confidence quality based on robustness, stability, and sensitivity to variations in prompts and answers.