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Clinical Mind AI is a Stanford-developed, international collaborative platform designed to strengthen clinical reasoning education through AI-simulated patient encounters across health professions.

Clinical Mind AI is a Stanford-developed, international collaborative platform designed to strengthen clinical reasoning education through AI-simulated patient encounters across health professions.
Our November session is Scaling Training Across GPUs and Nodes with speaker Zoe Ryan. This session focuses on efficient strategies for using multiple GPUs and nodes. We will discuss how to deploy strategies of data parallelism and model parallelism to scale to multiple GPUs through PyTorch, enabling faster training times and better model performance.
Location: CoDa W401 & Zoom
Refreshments and snacks provided
Please follow the link to register

Our November session is Scaling Training Across GPUs and Nodes with speaker Zoe Ryan. This session focuses on efficient strategies for using multiple GPUs and nodes. We will discuss how to deploy strategies of data parallelism and model parallelism to scale to multiple GPUs through PyTorch, enabling faster training times and better model performance.
Location: CoDa W401 & Zoom
Refreshments and snacks provided
Please follow the link to register
Sessions run Wednesdays from 4:30–5:30 PM in CoDa E160. Each session features a different Stanford speaker; talk titles are announced by the organizers.

Sessions run Wednesdays from 4:30–5:30 PM in CoDa E160. Each session features a different Stanford speaker; talk titles are announced by the organizers.
At last count, thirty lawsuits are challenging the legality of using in-copyright works to train generative AI models. While the lawsuits are still in early stages, fair use defenses are likely to be the main focus of judicial analyses of their merits. This talk will explain what judges have decided so far, that is, what issues are now out of those cases and what issues remain. Although generative AI developers point to some precedents that support their fair use defenses, this talk will explain why judges may find them not as helpful as the developers hope. At this point, some cases seem stronger than others. Not only are the plaintiffs seeking very large damage awards, but also some want courts to order models trained on the plaintiffs' data to be destroyed, and courts have power to issue such orders. The broader impacts of these lawsuits for academic research and development are at present invisible to the courts.
