HeyJaredGDG AI for Science
We are a community of practitioners that seek to advance the adoption of AI in Science and provide a platform for knowledge exchange, collaboration, and learning, fostering a thriving ecosystem of AI-powered scientific discovery.
- Indexed videos, last 90 days
- 4
- Latest publication
- Sep 25, 2026
- Audience
- ~984 subscribers
- Earliest in this view
- Jul 26, 2026
Latest videos
Gemini Robotics ER 2.0 Journal Club (opens the original)
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Join as we run through the [gemini-robotics-er-2-preview] (https://ai.google.dev/gemini-api/docs/robotics-overview) and what it means for robotics research and beyond. Embodied Reasoning, VLM Stacks, and Real-Time Simulation. Kunal Ostwald, University of Sydney PhD student and the group dive into Gemini Robotics ER 2.0 (Embodied Reasoning). The discussion explores how vision-language models (VLMs) act as the "brain" for physical robots—translating complex visual scenes, language, and high-level
AlphaGenome in Action (opens the original)
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Detecting DNA *variants* is relatively straightforward, predicting what they actually do at a molecular level is where genomics hits a wall! In this hands-on workshop hosted by GDG AI for Science, software & data engineer Farah Hammami @farahhammamiii breaks down how Google DeepMind’s AlphaGenome bridges the gap between sequence detection and functional biological prediction. Whether you're an ML engineer exploring bioinformatics or a computational biologist wanting to leverage transformer-based
From pixels to nature restoration (opens the original)
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Forests support the biodiversity on which humanity depends, but protecting them is often at odds with the agricultural demands of the world’s growing population. Ecological features like hedgerows and linear woodland offer a potential solution for enhancing carbon storage and biodiversity without displacing crops. We developed a high-resolution, deep-learning framework to map these features across agricultural land. We used Remote Sensing Foundations’ (RSF), part of Google Earth AI, to train our
Earth AI (opens the original)
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As planetary challenges multiply, this new planetary intelligence agent leverages multimodal AI, satellite imagery, and real-time environmental data to model, predict, and respond to global ecological shifts with unprecedented precision. Complex geospatial analytics, atmospheric simulations, and ecological modelling that previously required years of iteration can now be executed in minutes. As the field rapidly transitions toward hybrid AI frameworks, mastering these tools is critical to maintai
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Audience
~984 subscribers
Measured Sep 19, 2026
Source's subscribers, not the number who saw an individual piece.
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