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The AI Index, currently in its ninth year, tracks, collates, distills, and visualizes data relating to artificial intelligence.
The AI Index, currently in its ninth year, tracks, collates, distills, and visualizes data relating to artificial intelligence.
AI+Science: Accelerating Discovery is an interdisciplinary conference bringing together researchers across physics, mathematics, chemistry, biology, neuroscience, and more to examine how AI is reshaping scientific discovery.
AI+Science: Accelerating Discovery is an interdisciplinary conference bringing together researchers across physics, mathematics, chemistry, biology, neuroscience, and more to examine how AI is reshaping scientific discovery.
While Large Language Models (LLMs) show promise in many domains, relying on them for direct policy generation in games often results in illegal moves and poor strategic play.
While Large Language Models (LLMs) show promise in many domains, relying on them for direct policy generation in games often results in illegal moves and poor strategic play.
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.
