Foundation Models Tutorial, and Why Not to Fine Tune Them

Join Ananya Kumar, a fifth-year PhD student at Stanford University, as he delves into the world of Foundation Models.
In this informative video, he discusses his work on developing better algorithms for pre-training Foundation Models and their fine-tuning, especially in terms of robustness and safety. He provides a comprehensive tutorial on Foundation Models, their capabilities, and how they can be adapted for various tasks.
Ananya also highlights the potential risks and harms of these models, emphasizing the need for careful usage. He further discusses the Center for Research on Foundation Models at Stanford and its interdisciplinary approach towards the advancement and responsible use of Foundation Models. Towards the end, he provides a deep dive into a specific project on how fine-tuning can distort pre-trained features and underperform out of distribution.
This video is a must-watch for anyone interested in machine learning, AI models, and their real-world applications.
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[@user-li6vs9xr7i]

Hi, thanks for the nice sharing! Could you please provide the sildes you use in the video so I can further study?