Researchers at NYU Abu Dhabi have developed an AI system capable of translating spoken language into sign language. This innovative technology aims to enhance communication accessibility for individuals who are deaf or hard-of-hearing. The system leverages advancements in artificial intelligence, likely combining natural language processing for speech understanding and computer vision for sign generation. Why it matters: This development has the potential to significantly improve inclusion and communication for deaf communities within the Middle East and globally, bridging critical communication gaps.
A recent survey indicates that investors in the UAE are at the forefront of adopting artificial intelligence tools for managing their finances, surpassing global averages. The study highlights a significant shift towards leveraging AI for investment decisions and wealth management within the region. This trend suggests a strong embrace of technological advancements by the UAE's investment community. Why it matters: This leadership position underscores the UAE's progressive approach to financial technology and its potential to drive innovation in the global investment landscape.
The Open Arabic LLM Leaderboard (OALL) has been launched to benchmark Arabic language models, addressing the gap in resources for non-English NLP. It incorporates datasets like AlGhafa, ACVA, and translated versions of MMLU and EXAMS from the AceGPT suite. The leaderboard uses normalized log likelihood accuracy for tasks, built around HuggingFace’s LightEval framework. Why it matters: This initiative promotes research and development in Arabic NLP, serving over 380 million Arabic speakers by enhancing the evaluation and improvement of Arabic LLMs.
TII's Secure Systems Research Center (SSRC) has become a strategic member of RISC-V International to advance the development of open-source Instruction Set Architecture (ISA) for computer chips. SSRC aims to contribute to the RISC-V community by developing security and resilience features in processors and platforms, fostering innovation in end-to-end security. SSRC will conduct open source research to secure communications between edge devices and cloud infrastructure, and harden device hardware and software to prevent malware. Why it matters: This move enables the UAE to have greater control and independence in computing platform design, reducing reliance on proprietary architectures and enhancing security and resilience in critical infrastructure.
This paper introduces an explainable machine learning framework for early-stage chronic kidney disease (CKD) screening, specifically designed for low-resource settings in Bangladesh and South Asia. The framework utilizes a community-based dataset from Bangladesh and evaluates multiple ML classifiers with feature selection techniques. Results show that the ML models achieve high accuracy and sensitivity, outperforming existing screening tools and demonstrating strong generalizability across independent datasets from India, the UAE, and Bangladesh.
The Hala technical report introduces a family of Arabic-centric instruction and translation models developed using a translate-and-tune pipeline. A strong Arabic-English teacher model is compressed to FP8 and used to create bilingual supervision data. The LFM2-1.2B model is fine-tuned on this data and used to translate English instruction sets into Arabic, creating a million-scale corpus. Why it matters: The release of models, data, evaluation tools, and recipes will accelerate research and development in Arabic NLP, providing valuable resources for the community.
The UAE has launched K2 Think, a new open-source artificial intelligence model. This model is being presented as the world's most advanced in reasoning capabilities. It is designed to offer sophisticated cognitive problem-solving to the global AI community. Why it matters: This launch underscores the UAE's strategic commitment to advancing cutting-edge AI research and development, providing a significant open-source tool for complex AI applications.
The researchers introduce KAU-CSSL, the first continuous Saudi Sign Language (SSL) dataset focusing on complete sentences. They propose a transformer-based model using ResNet-18 for spatial feature extraction and a Transformer Encoder with Bidirectional LSTM for temporal dependencies. The model achieved 99.02% accuracy in signer-dependent mode and 77.71% in signer-independent mode, advancing communication tools for the SSL community.