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A Center for Neural Data Science Seminar Series

A Center for Neural Data Science Seminar Series
This session covers the foundational knowledge of GPUs, including their architecture, functionality, and applications in computing. It provides an introduction to GPU computing through the lens of the Marlowe SuperPod and prepares learners for advanced topics such as GPU-accelerated data science and machine learning.
Location: CoDa W401

This session covers the foundational knowledge of GPUs, including their architecture, functionality, and applications in computing. It provides an introduction to GPU computing through the lens of the Marlowe SuperPod and prepares learners for advanced topics such as GPU-accelerated data science and machine learning.
Location: CoDa W401
In mid-July, a working group focused on AI for the environment convened to outline future directions that would leverage AI to address pressing environmental challenges, ranging from biodiversity and conservation biology to water availability and sustainable communities. The group focused on the concept of building a thrivable planet for all species – not just one that is merely habitable. With the backdrop of the Stanford Educational Farm, we leveraged a human-centered design process to focus on how we might harness AI to uniquely address a range of stakeholder needs. Our objective was to develop an array of prototype projects that lead to insights about future directions for AI in the environmental and sustainability realms. Project prototypes included halting slavery in the seafood industry, intelligent tools for ensuring water and food security, and intelligent approaches for managing species migration. Based on these projects, we identified the following overarching themes that would be exciting to pursue through collaborative research: (1) Predicting, detecting and mitigating or incentivizing environmental transitions, (2) quantifying well-being and compatibility with one’s environment, (3) environmental justice and human rights, (4) opening of new data streams and achieving interoperability of existing data streams.
