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 researchers demonstrated a low-latency, multilingual multimodal AI system at GITEX that integrates speech, text, and visual capabilities for more lifelike human-machine conversation. The demo, led by Dr. Hisham Cholakkal, includes a mobile app where users can point their camera at an object and ask questions, receiving spoken answers in multiple languages. They are also integrating the model into a robot dog that can respond to voice commands. Why it matters: This work addresses key challenges in deploying LLMs to real-world applications in the Middle East, such as multilingual support and real-time responsiveness.
MBZUAI researchers have developed GeoPixel, a new multimodal model for pixel grounding in remote sensing images. GeoPixel associates individual pixels with object categories, enabling detailed image analysis by linking language to objects at the pixel level. The model was trained on a new dataset and benchmark, outperforming existing systems in precision. Why it matters: This advancement enhances the utility of remote sensing data for critical applications like environmental management and disaster response by providing more granular and accurate image interpretation.