The UAE's National Programme for Coders will train 20,000 students in coding across eight universities, including MBZUAI and Khalifa University. The program includes 500 training opportunities at local and international companies. Amazon, Huawei, and IBM will launch digital libraries providing resources on AI, data science, and other technologies. Why it matters: This initiative aims to bolster the UAE's AI talent pool and enhance graduates' competitiveness in the job market through practical coding skills.
MBZUAI researchers introduced Droid, a resource suite and detector family, at EMNLP 2025 designed to distinguish between AI-generated and human-written code. The project addresses the challenge of identifying AI-generated code in software development, considering the prevalence of AI-suggested code and the risks of obfuscated backdoors and feedback loops. DroidCollection includes over one million code samples across seven programming languages, three coding domains, and outputs from 43 different code models, including human-AI co-authored code and adversarially humanized machine code. Why it matters: This research is crucial for maintaining software security and integrity in the age of AI-assisted coding, providing a robust tool for detecting AI-generated code across diverse languages and domains.
MBZUAI researchers introduce Web2Code, a new large-scale dataset and evaluation framework for training and benchmarking multimodal LLMs on webpage understanding and HTML code generation. The dataset includes webpage images, HTML code, and QA pairs about webpage content. Experiments demonstrate the dataset's utility in webpage understanding, code generation, and general visual domain tasks, with code and data available on Github.
A study compared the vulnerability of C programs generated by nine state-of-the-art Large Language Models (LLMs) using a zero-shot prompt. The researchers introduced FormAI-v2, a dataset of 331,000 C programs generated by these LLMs, and found that at least 62.07% of the generated programs contained vulnerabilities, detected via formal verification. The research highlights the need for risk assessment and validation when deploying LLM-generated code in production environments.
KAUST's Visual Computing Center (VCC) is researching computer vision, image processing, and machine learning, with applications in self-driving cars, surveillance, and security. Professor Bernard Ghanem is working on teaching machines to understand visual data semantically, similar to how humans perceive the world. Self-driving cars use visual sensors to interpret traffic signals and detect obstacles, while computer vision also assists governments and corporations with security applications like facial recognition and detecting unattended luggage. Why it matters: Advancements in computer vision at KAUST can contribute to innovations in autonomous vehicles and enhance security measures in the region.
Students and teachers from Thuwal schools visited KAUST for computer-oriented activities on February 7. The activities included a practical computer coding lesson inspired by "Hour of Code," where participants used Mac computers to work through an online tutorial. Students and teachers also toured the supercomputing facilities in the KAUST Core Labs led by Bilel Hadri of the ECRC. Why it matters: Such outreach programs help promote STEM education and engagement with advanced computing resources among local students.
KAUST held its second hackathon and third NVIDIA workshop. Attendees listened to lectures from international experts. Participants worked on porting their scientific applications to a GPU accelerator. Why it matters: Such events help build regional expertise in accelerated computing and attract international collaboration.