This paper analyzes the energy consumption and carbon footprint of LLM inference in the UAE compared to Iceland, Germany, and the USA. The study uses DeepSeek Coder 1.3B and the HumanEval dataset to evaluate code generation. It provides a comparative analysis of geographical trade-offs for climate-aware AI deployment, specifically addressing the challenges and potential of datacenters in desert regions.
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.
Maryam Ahmed Hassani, an MBZUAI graduate and head of trends and innovation at Abu Dhabi’s Early Childhood Authority (ECA), is integrating AI into early childhood development to create efficient and human-focused systems. She aims to develop tools that personalize support for parents and predict developmental risks early on. Hassani also co-founded Zealous, an AI startup focused on AI code generation and testing, launching AI-native products including an AI-automated tester for websites and web apps. Why it matters: This highlights the growing application of AI in public sector challenges within the UAE, specifically in early childhood development and software development.