The country's Ministry of Communication and Digital Affairs said it will monitor xAI's newly implemented safety measures on an ongoing basis. Grok is once again available in Indonesia, after the country lifted its ban on the AI chatbot that was seen generating millions of sexualized deepfakes, thousands of which included children. The country's Ministry of Communication and Digital Affairs released a statement earlier today, which said X is allowed to resume service in Indonesia but will be subject to monitoring for any future violations. According to the Indonesian government agency, X provided a letter that detailed several implemented measures that prevent the misuse of its Grok chatbot. Alexander Sabar, the ministry's director general of digital space supervision, said in the statement that the agency will test the new measures on an ongoing basis and will ban Grok again if it's found spreading illegal content or violating the country's laws regarding children.

The UK's media regulator has opened a formal investigation into X. Malaysia and Indonesia are the first countries to block Grok, claiming that X's chatbot does not have sufficient safeguards in place to prevent explicit AI-generated deepfakes of women and children from being created and disseminated on X. Indonesia temporarily blocked access to Grok on Saturday, as did Malaysia on Sunday, the reports. Meanwhile, UK media regulator Ofcom has opened a formal investigation into X under the Online Safety Act. The government sees non-consensual sexual deepfakes as a serious violation of human rights, dignity and the safety of citizens in the digital space, Indonesia's Communication and Digital Affairs Minister Meutya Hafid said in a statement. Officials in the country said initial findings showed that Grok lacks effective controls to prevent users from creating and sharing sexually explicit deepfakes based on photos of Indonesian residents.

Malaysia and Indonesia are the first to block Grok over explicit deepfakes that the chatbot has been generating. The UK's media regulator has opened a formal investigation into X under the Online Safety Act. There have been deeply concerning reports of the Grok AI chatbot account on X being used to create and share undressed images of people -- which may amount to intimate image abuse or pornography -- and sexualized images of children that may amount to child sexual abuse material (CSAM), Ofcom said. The investigation will focus on whether X has has complied with its duties to protect people in the UK from content that is illegal in the UK. That includes whether X is taking appropriate measures to prevent UK users from seeing priority illegal content, such as CSAM and non-consensual intimate images; if the platform is removing illegal content quickly after becoming aware of it; and whether X carried out an updated risk assessment before making any significant changes to the platform.

With human help, the mammals can defy'the world's most fundamental natural boundaries.' Breakthroughs, discoveries, and DIY tips sent every weekday. Despite not exactly being world-renowned swimmers, pigs have spread across the Asia-Pacific region for thousands of years . With the genetic and archeological data from over 700 pigs, a team of scientists documented how people helped the mammals make their way across thousands of miles. "This research reveals what happens when people transport animals enormous distances, across one of the world's most fundamental natural boundaries," evolutionary geneticist and study co-author author Dr. David Stanton of the University of Cardiff and Queen Mary University of London said in a statement. "These movements led to pigs with a melting pot of ancestries. These patterns were technically very difficult to disentangle, but have ultimately helped us understand how and why animals came to be distributed across the Pacific islands."

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Scores are calculated by giving a weight of 1 for applicable, 0.5 for conditionally applicable, and 0 for incorrect responses.

To serve global users safely and productively, LLMs need culture-specific knowledge that might not be learned during pre-training. How do we find such knowledge that is (1) salient to in-group users, but (2) unknown to LLMs? The most common solutions are single-initiative: either researchers define challenging questions that users passively answer (traditional annotation), or users actively produce data that researchers structure as benchmarks (knowledge extraction). The process would benefit from mixed-initiative collaboration, where users guide the process to meaningfully reflect their cultures, and LLMs steer the process towards more challenging questions that meet the researcher's goals. We propose a mixed-initiative methodology called CultureCartography. Here, an LLM initializes annotation with questions for which it has low-confidence answers, making explicit both its prior knowledge and the gaps therein. This allows a human respondent to fill these gaps and steer the model towards salient topics through direct edits. We implement this methodology as a tool called CultureExplorer. Compared to a baseline where humans answer LLM-proposed questions, we find that CultureExplorer more effectively produces knowledge that leading models like DeepSeek R1 and GPT-4o are missing, even with web search. Fine-tuning on this data boosts the accuracy of Llama-3.1-8B by up to 19.2% on related culture benchmarks.

Scores are calculated by giving a weight of 1 for applicable, 0.5 for conditionally applicable, and 0 for incorrect responses.

Despite rapid progress in vision-language and large language models (VLMs and LLMs), their effectiveness for AI-driven educational assessment in real-world, underrepresented classrooms remains largely unexplored. We evaluate state-of-the-art VLMs and LLMs on over 14K handwritten answers from grade-4 classrooms in Indonesia, covering Mathematics and English aligned with the local national curriculum. Unlike prior work on clean digital text, our dataset features naturally curly, diverse handwriting from real classrooms, posing realistic visual and linguistic challenges. Assessment tasks include grading and generating personalized Indonesian feedback guided by rubric-based evaluation. Results show that the VLM struggles with handwriting recognition, causing error propagation in LLM grading, yet LLM feedback remains pedagogically useful despite imperfect visual inputs, revealing limits in personalization and contextual relevance.

Context: Financial system stability is determined by the condition of the banking system. A bank failure can destroy the stability of the financial system, as banks are subject to systemic risk, affecting not only individual banks but also segments or the entire financial system. Calculating the probability of a bank going bankrupt is one way to ensure the banking system is safe and sound. Existing literature and limitations: Statistical models, such as Altman's Z-Score, are one of the common techniques for developing a bankruptcy prediction model. However, statistical methods rely on rigid and sometimes irrelevant assumptions, which can result in low forecast accuracy. New approaches are necessary. Objective of the research: Bankruptcy models are developed using machine learning techniques, such as logistic regression (LR), random forest (RF), and support vector machines (SVM). According to several studies, machine learning is also more accurate and effective than statistical methods for categorising and forecasting banking risk management. Present Research: The commercial bank data are derived from the annual financial statements of 44 active banks and 21 bankrupt banks in Turkey from 1994 to 2004, and the rural bank data are derived from the quarterly financial reports of 43 active and 43 bankrupt rural banks in Indonesia between 2013 and 2019. Five rural banks in Indonesia have also been selected to demonstrate the feasibility of analysing bank bankruptcy trends. Findings and implications: The results of the research experiments show that RF can forecast data from commercial banks with a 90% accuracy rate. Furthermore, the three machine learning methods proposed accurately predict the likelihood of rural bank bankruptcy. Contribution and Conclusion: The proposed innovative machine learning approach help to implement policies that reduce the costs of bankruptcy.