KAUST has been selected as the first FIFA Research Institute in the Middle East and Asia. KAUST will apply its research expertise to advance football-related studies, initially focusing on developing datasets that enable deeper insights into the game. The collaboration’s first project focuses on developing AI algorithms to analyze historical FIFA World Cup broadcast footage, while the second project leverages player and ball tracking data from the FIFA World Cup 2022™ Qatar and the FIFA Women’s World Cup 2023™ Australia & New Zealand. Why it matters: This partnership strengthens the intersection of sport, academia, and industry in the region through high-impact scientific inquiry.
Researchers from KAUST and KACST have developed a quantum random number generator (QRNG) that is almost 1000 times faster than existing QRNGs. The device utilizes micro-LEDs and advanced post-processing algorithms and has passed randomness tests by the National Institute of Standards and Technology. The QRNG's portability and high generation rate will benefit industries such as health, finance, and defense. Why it matters: This advancement significantly strengthens data security capabilities in Saudi Arabia, aligning with Vision 2030 goals for technological leadership and innovation.
The Qatar Computing Research Institute (QCRI) has released SpokenNativQA, a multilingual spoken question-answering dataset for evaluating LLMs in conversational settings. The dataset contains 33,000 naturally spoken questions and answers across multiple languages, including low-resource and dialect-rich languages. It aims to address the limitations of text-based QA datasets by incorporating speech variability, accents, and linguistic diversity. Why it matters: This benchmark enables more robust evaluation of LLMs in speech-based interactions, particularly for Arabic dialects and other low-resource languages.
Hamad Bin Khalifa University's Qatar Computing Research Institute (QCRI) introduced Fanar, an Arabic-centric multimodal generative AI platform featuring the Fanar Star (7B) and Fanar Prime (9B) Arabic LLMs. These models were trained on nearly 1 trillion tokens and are designed to address different prompts through a custom orchestrator. Fanar includes a customized Islamic RAG system, a Recency RAG, bilingual speech recognition, and an attribution service for content verification, sponsored by Qatar's Ministry of Communications and Information Technology. Why it matters: The platform signifies a major step towards sovereign AI development in Qatar, providing advanced Arabic language capabilities and addressing regional needs.
KAUST, NEOM’s Education, Research, and Innovation Foundation (ERIF), and ENOWA have formed a partnership to support Saudi Arabia’s hydrogen economy. ERIF has sponsored three strategic projects under its Hydrogen and e-Fuels Applied Research Institute (HEFARI) with KAUST researchers focusing on developing hydrogen as a renewable energy vector. The projects cover carbon-neutral fuels, cost-effective electrolyzer technologies, and lowering emissions from green ammonia. Why it matters: This collaboration aims to establish Saudi Arabia as a leader in green hydrogen technologies and sustainable fuel production, aligning with the Kingdom's decarbonization goals.
Qatar Computing Research Institute (QCRI) has developed NatiQ, an end-to-end text-to-speech (TTS) system for Arabic utilizing encoder-decoder architectures. The system employs Tacotron-based models and Transformer models to generate mel-spectrograms, which are then synthesized into waveforms using vocoders like WaveRNN, WaveGlow, and Parallel WaveGAN. Trained on in-house speech data featuring a neutral male voice (Hamza) and an expressive female voice (Amina), NatiQ achieves a Mean Opinion Score (MOS) of 4.21 and 4.40, respectively. Why it matters: This research advances Arabic language technology, providing high-quality TTS synthesis that can enhance accessibility and usability of digital content for Arabic speakers.
The Qatar Computing Research Institute (QCRI) has released QASR, a 2,000-hour transcribed Arabic speech corpus collected from Aljazeera news broadcasts. The dataset features multi-dialect speech sampled at 16kHz, aligned with lightly supervised transcriptions and linguistically motivated segmentation. QCRI also released a 130M word dataset to improve language model training. Why it matters: QASR enables new research in Arabic speech recognition, dialect identification, punctuation restoration, and other NLP tasks for spoken data.
CSAIL (MIT's Computer Science and Artificial Intelligence Laboratory) and the Qatar Computing Research Institute (QCRI) have announced a new research collaboration. This partnership aims to foster joint research projects and facilitate knowledge exchange between the two prominent AI research institutions. The collaboration is expected to leverage expertise from both sides in various AI domains. Why it matters: This partnership signifies a strengthening of AI research ties between a leading US institution and a major Middle Eastern research hub, potentially driving advancements relevant to the region and the broader AI field.