MBZUAI's Professor Le Song is developing an AI-driven simulation to model the human body at societal, organ, tissue, cellular, and molecular levels. The goal is to reduce the time and cost associated with bringing new medicines to market by removing the need for wet lab biological research. Song aims to create a comprehensive model using machine learning. Why it matters: This research could revolutionize drug discovery in the region by accelerating the development process and reducing reliance on traditional research methods.
Researchers at the Rosalind Franklin Institute are using generative AI, including GANs, to augment limited biological datasets, specifically mirtron data from mirtronDB. The synthetic data created mimics real-world samples, facilitating more comprehensive training of machine learning models, leading to improved mirtron identification tools. They also plan to apply Large Language Models (LLMs) to predict unknown patterns in sequence and structure biology problems. Why it matters: This research explores AI techniques to tackle data scarcity in biological research, potentially accelerating discoveries in noncoding RNA and transposable elements.
MBZUAI Assistant Professor Qirong Ho is researching AI operating systems to standardize algorithms and enable non-experts to create AI applications reliably. He emphasizes that countries mastering mass production of AI systems will benefit most from the Fourth Industrial Revolution. Ho is co-founder and CTO at Petuum Inc., an AI startup creating standardized building blocks for affordable and scalable AI production. Why it matters: This research aims to democratize AI development and promote widespread adoption across industries in the UAE and beyond.
MBZUAI's Qirong Ho and colleagues are developing an Artificial Intelligence Operating System (AIOS) for decarbonization, aiming to reduce energy waste in AI development. The AIOS focuses on improving communication efficiency between machines during AI model training, as inefficient communication leads to prolonged tasks and increased energy consumption. This system addresses the high computing power demands of large language models like ChatGPT and LLaMA-2. Why it matters: By optimizing energy usage in AI development, the AIOS could significantly reduce the carbon footprint of AI technologies in the region and globally.
A KAUST team led by Xin Gao developed an AI model for COVID-19 detection from CT scans, addressing limitations of existing methods. The model incorporates a novel embedding strategy, a CT scan simulator, and a 2.5D deep-learning algorithm. Tested at King Faisal Specialist Hospital, the model demonstrated high accuracy in detecting COVID-19 cases. Why it matters: This research provides a valuable tool for rapid and accurate COVID-19 diagnosis in the region, especially in early-stage infections, improving healthcare outcomes.
MBZUAI hosted a two-day workshop on "Big Model AI in Drug Design" starting February 20, 2023. The workshop featured presentations from researchers in public and private institutions working on AI and health. MBZUAI Adjunct Professor Eran Segal opened the workshop with a talk on the Human Phenotype Project. Why it matters: The event highlights the growing interest and activity in applying AI, particularly large models, to advance drug discovery and personalized medicine within the UAE's research ecosystem.