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Smart replies, writing enhancements, and virtual assistants powered by artificial intelligence language technologies are increasingly being integrated into consumer products and everyday experiences.
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.
This research explores both the potential and risks of AI-mediated communication (AI-MC) technologies such as GPT-4—specifically in the political sphere—through a series of experiments designed to assess the possible uses and misuses of AI-MC. The initial part of the research evaluates whether human-AI collaboration can increase legislator responsiveness by studying citizen responses to AI-generated tweets and email correspondence. The findings point to the importance of disclosure, transparency, and human-in-the-loop accountability for AI-mediated political communication. The research then turns to the plausibility of misuse by actors seeking to influence the democratic process. It shares results from a field experiment on legislators, highlights the challenge these technologies present to democratic representation, and suggests techniques elected officials might employ to guard against AI-sourced astroturfing.
