MBZUAI President Eric Xing delivered a talk at Carnegie Mellon University on May 13, 2022, titled “From Learning, to Meta-Learning, to Lego-Learning — theory, systems, and engineering.” Xing discussed the development of a standard model for learning, inspired by the standard model in physics, which aims to unify various machine learning paradigms. Before joining MBZUAI, Xing was a professor at CMU and founder of Petuum Inc., an AI development platform company. Why it matters: This talk highlights MBZUAI's leadership in advancing theoretical frameworks for machine learning and its commitment to unifying different AI approaches.
MBZUAI Professor Salman Khan is researching continuous, lifelong learning systems for computer vision, aiming to mimic human learning processes like curiosity and discovery. His work focuses on learning from limited data and adversarial robustness of deep neural networks. Khan, along with MBZUAI professors Fahad Khan and Rao Anwer, and partners from other universities, presented research at CVPR 2022. Why it matters: This research has the potential to significantly improve the ability of AI systems to understand and adapt to the real world, enabling more intelligent autonomous systems.
Michael Yu Wang, Chair Professor and Founding Dean of the School of Engineering at Great Bay University, argues for combining "good old fashioned engineering" (GOFE) with learning-based approaches like LLMs for robot skill acquisition, particularly in manipulation. He suggests a modular framework that integrates engineering principles with learning, drawing inspiration from human hand-eye coordination and tactile perception. Wang emphasizes the need to address engineering features of robot tactile sensors, such as spatial and temporal resolutions, to achieve human-like robot manipulation skills. Why it matters: This perspective highlights the importance of hybrid approaches combining traditional engineering with modern AI for advancing robotics, especially in complex manipulation tasks relevant to industries in the GCC region.
Professor Hava Siegelmann, a computer science expert, is researching lifelong learning AI, drawing inspiration from the brain's abstraction and generalization capabilities. The research aims to enable intelligent systems in satellites, robots, and medical devices to adapt and improve their expertise in real-time, even with limited communication and power. The goal is to develop AI systems applicable for far edge computing that can learn in runtime and handle unanticipated situations. Why it matters: This research could lead to more resilient and adaptable AI systems for critical applications in remote and resource-constrained environments, with potential benefits for various sectors in the Middle East.
A recent talk at MBZUAI discussed "Green Learning" and Operational Neural Networks (ONNs) as efficient alternatives to CNNs. ONNs use "nodal" and "pool" operators and "generative neurons" to expand neuron learning capacity. Moncef Gabbouj from Tampere University presented Self-Organized ONNs (Self-ONNs) and their signal processing applications. Why it matters: Exploring more efficient AI models is crucial for sustainable development of AI in the region, as it addresses computational resource constraints and promotes broader accessibility.