Dubai, UAE, April 22, 2025: Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) will participate as an official partner at Machines Can See 2025 (MCS 2025), taking place April 23–24 during Dubai AI Week. The event, hosted at the Museum of the Future and Emirates Towers, will gather more than 2,000 experts and enthusiasts focused on building a safer, more sustainable world through AI.
“As an official partner of Machines Can See, MBZUAI is proud to contribute towards building an AI ecosystem aligned with the UAE’s ambitions for global leadership in science and technology,” said Ivan Laptev, Professor of Computer Vision at MBZUAI and Co-Founder of Machines Can See. “Our research and innovations across all AI disciplines inspire students and researchers to unlock the full potential of this technology. We’re excited to connect with the wider community and share what we’ve been working on in robotics and computer vision, showing how the university is making progress in building smart, AI-powered systems.”
This advanced platform segments the brain into 32 structures and analyzes over 100 radiomics features per region to detect subtle abnormalities. It integrates imaging, connectivity, and genetic data to support personalized risk assessment and precision diagnostics.
Using the BiMediX2 model, this multimodal virtual assistant can answer medical questions in Arabic and English, interpret medical images, and interact via speech. It addresses healthcare access challenges across the Middle East and Africa and won Meta’s Llama Impact Innovation Award.
Created by the MBZUAI Metaverse Center, Voodoo XP is a one-shot face re-enactment tool that builds 3D avatars from a single image. It enables immersive, realistic communication via camera or VR headset.
LAIKA is a robot dog that demonstrates AI-powered mobility and reasoning. It can respond to voice commands, navigate, and describe its observations, making it ideal for safety, healthcare, and industrial applications.
Xiuying Chen explains how AnomalyGen is using foundation models to help household robots anticipate dangerous scenarios.
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Mohamed El Amine Boudjoghra explains how his team have improved machines' speed and accuracy in recognizing objects.