Saudi Arabia has launched its National Strategy for Data and AI (NSDAI), outlining the Kingdom's ambition to become a global leader in the field. The strategy focuses on creating a thriving data and AI ecosystem, developing local talent, and attracting foreign investment. It aims to drive economic diversification and improve the quality of life for citizens. Why it matters: The NSDAI signals a strong commitment from Saudi Arabia to become a major player in the global AI landscape, potentially influencing regional development and investment.
This paper introduces AraDhati+, a new comprehensive dataset for Arabic subjectivity analysis created by combining existing datasets like ASTD, LABR, HARD, and SANAD. The researchers fine-tuned Arabic language models including XLM-RoBERTa, AraBERT, and ArabianGPT on AraDhati+ for subjectivity classification. An ensemble decision approach achieved 97.79% accuracy. Why it matters: The work addresses the under-resourced nature of Arabic NLP by providing a new dataset and demonstrating strong results in subjectivity classification, advancing sentiment analysis capabilities for the Arabic language.
Saudi Arabia has launched HUMAIN Chat, a new conversational artificial intelligence application. The platform is being marketed as the first conversational Arabic AI app developed in the region. This launch signifies a key step in the Kingdom's efforts to advance its digital capabilities and AI offerings. Why it matters: The introduction of a dedicated Arabic conversational AI platform can significantly enhance digital interaction for Arabic speakers and accelerate the development of localized AI solutions in the Middle East.
This paper presents a UI-level evaluation of ALLaM-34B, an Arabic-centric LLM developed by SDAIA and deployed in the HUMAIN Chat service. The evaluation used a prompt pack spanning various Arabic dialects, code-switching, reasoning, and safety, with outputs scored by frontier LLM judges. Results indicate strong performance in generation, code-switching, MSA handling, reasoning, and improved dialect fidelity, positioning ALLaM-34B as a robust Arabic LLM suitable for real-world use.
The UAE is making major investments in AI to diversify its economy and exert global influence. These investments include partnerships with global tech companies, development of AI infrastructure, and the establishment of AI-focused institutions. The UAE aims to become a leader in AI research and development, attracting talent and fostering innovation. Why it matters: This strategic focus on AI could reshape the UAE's economic and geopolitical role in the region and beyond.
The QU-NLP team presented their approach to the QIAS 2025 shared task on Islamic Inheritance Reasoning, fine-tuning the Fanar-1-9B model using LoRA and integrating it into a RAG pipeline. Their system achieved an accuracy of 0.858 on the final test, outperforming models like GPT 4.5, LLaMA, and Mistral in zero-shot settings. The system particularly excelled in advanced reasoning, achieving 97.6% accuracy. Why it matters: This demonstrates the effectiveness of domain-specific fine-tuning and retrieval augmentation for Arabic LLMs in complex reasoning tasks, even surpassing frontier models.
This paper introduces Saudi-Dialect-ALLaM, a LoRA fine-tuned version of the Saudi Arabian foundation model ALLaM-7B-Instruct-preview, designed to improve the generation of Saudi dialects (Najdi and Hijazi). The model is trained on a private dataset of 5,466 synthetic instruction-response pairs, with two variants explored: Dialect-Token and No-Token training. Results indicate that the Dialect-Token model achieves superior dialect control and fidelity compared to generic instruction models, although the dataset and model weights are not released.
Tajikistan has unveiled plans to establish Central Asia's first regional Artificial Intelligence (AI) center. The initiative aims to foster AI development, research, and collaboration across the Central Asian region. This center is expected to serve as a hub for innovation, knowledge exchange, and capacity building in AI technologies. Why it matters: This move could position Tajikistan as a leader in AI for Central Asia, significantly boosting regional technological advancement and potentially attracting international investment and expertise to the region.
This paper benchmarks the performance of large language models (LLMs) on Arabic medical natural language processing tasks using the AraHealthQA dataset. The study evaluated LLMs in multiple-choice question answering, fill-in-the-blank, and open-ended question answering scenarios. The results showed that a majority voting solution using Gemini Flash 2.5, Gemini Pro 2.5, and GPT o3 achieved 77% accuracy on MCQs, while other LLMs achieved a BERTScore of 86.44% on open-ended questions. Why it matters: The research highlights both the potential and limitations of current LLMs in Arabic clinical contexts, providing a baseline for future improvements in Arabic medical AI.
KAUST, Tufts, and JIHS researchers created pangenome graphs using Saudi and Japanese samples, named JaSaPaGe. These graphs address the underrepresentation of these populations in existing pangenome databases, which are used as references for understanding individual DNA. The population-specific pangenomes are expected to improve variant calling and diagnostic accuracy for genetic disorders in these groups. Why it matters: This work promotes precision medicine and reduces diagnostic gaps for underrepresented populations by providing more relevant genetic baselines.
A KAUST-led study in Nature proposes reversing land degradation by 2050 through increased sustainable seafood production, reduced food waste, and land restoration. The study suggests straightforward measures like modifying economic incentives and promoting sustainable aquaculture policies. Researchers estimate these policies could save a land area roughly the size of Africa. Why it matters: The KAUST-led research offers a tangible blueprint for addressing critical food security challenges in arid regions like Saudi Arabia and globally.
The UAE government is developing large language models (LLMs) specifically for the Arabic language, with a target training dataset of 20 million words. This initiative aims to overcome the underrepresentation of Arabic in existing AI models. The project seeks to enhance AI's ability to understand and generate nuanced Arabic content. Why it matters: A national Arabic LLM can enable culturally relevant AI applications across various sectors in the region, from education to government services.
KAUST researchers have developed an AI system for the Saudi Geological Survey (SGS) to improve the scientific understanding of seismic activity in Saudi Arabia. The AI system helps the SGS analyze swarm earthquakes, which are common in volcanic regions and difficult to decipher using conventional methods. The system allows for a more reliable survey of seismic regions, better infrastructure planning, and improved building codes. Why it matters: The AI system enhances Saudi Arabia's ability to monitor and respond to seismic events, contributing to public safety and infrastructure resilience.