In the last couple of months, we have seen a lot of people and companies sharing and open-sourcing various kinds of LLMs and datasets, which is awesome. However, from a research perspective, it felt more like a race to be out there first (which is understandable) versus doing principled analyses.
Delightful to read as always. I enjoy the concise summarizations of recent AI development, especially in this super fast pace era. Also thank you for sharing your thoughts on a lot of these problems. This opens up a lot of discussions and potential research areas.
Great insights on this AI issue. While the readings is a well fit-purpose for researchers and alike to allow replicating work, I was wondering in some sort of as an extended arm on these LLMs topics, if a pragmatic approach on how to implement all of these papers/research work via applications (streamlit, gradio, etc., just to name a few) can be covered as well with practical examples.
Delightful to read as always. I enjoy the concise summarizations of recent AI development, especially in this super fast pace era. Also thank you for sharing your thoughts on a lot of these problems. This opens up a lot of discussions and potential research areas.
Great insights on this AI issue. While the readings is a well fit-purpose for researchers and alike to allow replicating work, I was wondering in some sort of as an extended arm on these LLMs topics, if a pragmatic approach on how to implement all of these papers/research work via applications (streamlit, gradio, etc., just to name a few) can be covered as well with practical examples.