KAUST ·
Lea Sublett, Manager of KAUST Alumni Affairs, developed a love for travel and diverse cultures through her upbringing. She initially pursued journalism but found herself working in alumni relations at universities, including five Australian universities before KAUST. Sublett's career has allowed her to meet alumni worldwide and learn about their experiences. Why it matters: This profile highlights the international and diverse community fostered by KAUST, emphasizing the importance of cultural exchange and global engagement in its mission.
KAUST · alumni affairs · Lea Sublett · international · Australia
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KAUST ·
Fahad Alsherehey, VP at SABIC, spoke at KAUST's Winter Enrichment Program (WEP) about authentic leadership. He cited SABIC's founding as an example of how leadership can turn challenges into opportunities. Alsherehey emphasized the difference between leadership and management, advocating for listening to one's team. Why it matters: The talk highlights the importance of leadership and vision in driving technological and economic development in Saudi Arabia.
MBZUAI ·
A delegation from the Australia UAE Business Council visited MBZUAI to discuss potential AI collaborations between the two countries. The council members, led by H.E. Abdulla Ali Alsubousi, toured MBZUAI’s campus and facilities and discussed the importance of connecting academia with industry. The Australia UAE Business Council recently launched its Artificial Intelligence Technologies Working Group to investigate AI technology enablers across Australia and the UAE. Why it matters: This partnership signals growing international interest in MBZUAI and the UAE's AI ecosystem, potentially fostering joint research and development initiatives.
arXiv ·
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.