MBZUAI researchers presented a new approach to video question answering at ICCV 2023. The method leverages insights from analyzing still images to understand video content, potentially reducing the computational resources needed for training video question answering models. Guangyi Chen, Kun Zhang, and colleagues aim to apply pre-trained image models to understand video concepts. Why it matters: This research could lead to more efficient and accessible video analysis tools, benefiting fields like healthcare and security where video data is abundant.
This paper introduces a novel two-step method for predicting urban expansion using time-series satellite imagery. The approach combines semantic image segmentation with a CNN-LSTM model to learn temporal features. Experiments on satellite images from Riyadh, Jeddah, and Dammam in Saudi Arabia demonstrate improved performance compared to existing methods based on Mean Square Error, Root Mean Square Error, Peak Signal to Noise Ratio, Structural Similarity Index, and overall classification accuracy.
This article discusses how AI has been portrayed in cinema, from early automatons in Metropolis to modern depictions of digital consciousness in Blade Runner and Terminator. It explores themes such as autonomy, intelligence, and responsibility. The films mentioned capture themes that still shape public imagination. Why it matters: Examining AI through the lens of cinema provides insights into societal hopes, fears, and questions about humanity's relationship with intelligent machines, influencing public perception and ethical considerations.
Estelle Metayer, a public speaker and professor at McGill University, spoke at KAUST as part of the University’s 2017 Winter Enrichment Program. Her talk focused on the future of technology and science. The event took place in the University's Auditorium on January 18. Why it matters: Such events at KAUST contribute to knowledge dissemination and engagement with global experts on emerging technologies.
Technology Innovation Institute's (TII) Directed Energy Research Center (DERC) is integrating machine learning (ML) techniques into signal processing to accelerate research. One project used convolutional neural networks to predict COVID-19 pneumonia from chest x-rays with 97.5% accuracy. DERC researchers also demonstrated that ML-based signal and image processing can retrieve up to 68% of text information from electromagnetic emanations. Why it matters: This adoption of ML for signal processing at TII highlights the potential for advanced AI techniques to enhance research and security applications in the UAE.