Jürgen Schmidhuber has been appointed as the Director of the KAUST AI Initiative. Schmidhuber is known for his contributions to deep learning and artificial neural networks, and co-founded the company NNAISENSE. At KAUST, he will focus on faculty recruitment, educational programs, and collaboration with public and private sectors. Why it matters: The appointment of a leading AI researcher signals KAUST and Saudi Arabia's commitment to advancing AI research and its application to key national projects.
KAUST showcased its AI initiatives at the 2022 Global AI Summit in Riyadh, highlighting its efforts to increase AI capacity and innovation in Saudi Arabia. Jurgen Schmidhuber, Director of the KAUST AI Initiative, addressed attendees on AI and deep learning, while Provost Lawrence Carin and Deputy Director Bernard Ghanem discussed AI talent development. KAUST is partnering with public and private sector institutions to embed AI in key areas such as security, energy, data analytics, and health. Why it matters: This participation reinforces KAUST's central role in advancing AI research and development within the Kingdom and aligns with Saudi Arabia's broader vision for technological leadership.
KAUST has established a Center of Excellence (CoE) for Generative AI, chaired by Professor Bernard Ghanem and co-chaired by Professor Jürgen Schmidhuber. The center will focus on scientific research, commercial innovation, and talent development in GenAI, aligning with Saudi Arabia's Vision 2030 goals. The CoE aims to impact Saudi Arabia's four RDI priorities: Health and Wellness, Sustainable Environment, Energy and Industrial Leadership, and Economies of the Future. Why it matters: The KAUST center aims to position Saudi Arabia as a global leader in generative AI, addressing the need for specialized expertise and infrastructure while contributing to the Kingdom's economic diversification.
KAUST has launched four Centers of Excellence (CoEs) focusing on Health, Sustainable Environment, Energy, and Economies of the Future, aligning with Saudi Arabia’s Vision 2030. One CoE, chaired by Professor Bernard Ghanem and co-chaired by Professor Juergen Schmidhuber, will focus on generative AI. These centers aim to deliver impactful solutions that directly contribute to national economic objectives. Why it matters: This initiative signifies a major push towards applied AI research and development within Saudi Arabia, particularly in generative AI, renewable energy, food security, and smart health.
Pascal Fua from EPFL presented an approach to implementing convolutional neural nets that output complex 3D surface meshes. The method overcomes limitations in converting implicit representations to explicit surface representations. Applications include single view reconstruction, physically-driven shape optimization, and bio-medical image segmentation. Why it matters: This research advances geometric deep learning by enabling end-to-end trainable models for 3D surface mesh generation, with potential impact on various applications in computer vision and biomedical imaging in the region.
Patrick van der Smagt, Director of AI Research at Volkswagen Group, discussed the use of generative machine learning models for predicting and controlling complex stochastic systems in robotics. The talk highlighted examples in robotics and beyond and addressed the challenges of achieving quality and trust in AI systems. He also mentioned his involvement in a European industry initiative on trust in AI and his membership in the AI Council of the State of Bavaria. Why it matters: Understanding control in robotics, along with trust in AI, are key issues for further development of autonomous systems, especially in industrial applications within the GCC region.
A professor from Nanyang Technological University (NTU), Singapore gave a talk at MBZUAI about "Just-Noticeable Difference (JND)" models in visual intelligence. The talk covered visual JND models, research and applications, and future opportunities for JND modeling. JND can help tackle big data challenges with limited resources by focusing on user-centric and green systems. Why it matters: Exploring JND could lead to advancements in AI applications related to visual signal processing, image synthesis, and generative AI in the region.
KAUST Professor Peter Richtárik received a Distinguished Speaker Award at the Sixth International Conference on Continuous Optimization (ICCOPT 2019) in Berlin. Richtárik's lecture series, totaling six hours, focused on stochastic gradient descent (SGD) methods, drawing from recent research by his KAUST group. He highlighted key principles and new variants of SGD, the key method for training modern machine learning models. Why it matters: This award recognizes KAUST's contribution to fundamental machine learning optimization, which is critical for advancing AI in the region.