Harvard Professor David C. Parkes is leading a session on AI, Machine Learning, and Economics for the inaugural cohort of the MBZUAI Executive Program. This program includes 42 participants, including ministers and C-suite executives, and spans 12 weeks. The program aims to support the UAE's AI leadership mission through education and capacity building. Why it matters: This highlights the UAE's ongoing efforts to attract global AI expertise and develop local leadership in the field, furthering its national AI strategy.
Dr. David Paredes from Drexel and Purdue Universities conducted a workshop on sustaining creativity at KAUST's 2015 Winter Enrichment Program. The workshop aimed to inspire students to be creative and remember why they entered their fields. Students used the Reisman Diagnostic Creativity Assessment tool to evaluate their creative strengths in ideation, risk tolerance, solution focus, and motivation. Why it matters: Such workshops, while not directly advancing AI research, foster a culture of innovation and risk-taking that is crucial for breakthroughs in AI and other STEM fields in the region.
MBZUAI has appointed Professor Timothy Baldwin as Associate Provost and acting chair of its new NLP Department. Baldwin will focus on strengthening the curriculum and building a world-class faculty team. He previously spent 17 years at the University of Melbourne. Why it matters: The recruitment signals MBZUAI's commitment to becoming a leading center for NLP research and education in the region.
This paper introduces DaringFed, a novel dynamic Bayesian persuasion pricing mechanism for online federated learning (OFL) that addresses the challenge of two-sided incomplete information (TII) regarding resources. It formulates the interaction between the server and clients as a dynamic signaling and pricing allocation problem within a Bayesian persuasion game, demonstrating the existence of a unique Bayesian persuasion Nash equilibrium. Evaluations on real and synthetic datasets demonstrate that DaringFed optimizes accuracy and convergence speed and improves the server's utility.
Qingbiao Li from the Oxford Robotics Institute is researching decentralized multi-robot coordination using Graph Neural Networks (GNNs). The approach builds an information-sharing mechanism within a decentralized multi-robot system through GNNs and imitation learning. It also uses visual machine learning-assisted navigation with panoramic cameras to guide robots in unseen environments. Why it matters: This research could improve the effectiveness of automated mobile robot systems in urban rail transit and warehousing logistics in the GCC region, where smart city initiatives are growing.
Dr. Yali Du from King's College London will give a presentation on learning to cooperate in multi-agent systems. Her research focuses on enabling cooperative and responsible behavior in machines using reinforcement learning and foundation models. She will discuss enhancing collaboration within social contexts, fostering human-AI coordination, and achieving scalable alignment. Why it matters: This highlights the growing importance of research into multi-agent systems and human-AI interaction, crucial for developing AI that integrates effectively and ethically into society.