1/ This just in. A new successful paradigm for building AI systems has emerged, called "Foundation Model". According to its inventors
crfm-stanford.github.io, it works as follows: "Train one model on a huge amount of data and adapt it to many applications." Not only is it
crfm-stanford.github.io, it works as follows: "Train one model on a huge amount of data and adapt it to many applications." Not only is it
2/ seen "as the beginnings of a sweeping paradigm shift in AI", but a whole Center has been erected in its honor, dozens of prominent researchers, post-docs and PhD students has joined its staff, and an interdisciplinary symposium has been announced. We, foot-soldiers in the
3/ trenches of AI research are asking, of course: "What is it?" or "What is the scientific principle by which 'Foundation models' can circumvent the theoretical limitations of data-centric methods as we know them, especially those that hinder generalization across environments?"
4/ I have tried to pin point the principle, but ended up in failure; the limitations as we know them are not mentioned in the papers I examined, nor are concepts such as "causation" "Data Fusion" "Transportability" "data-centric" etc. Can some readers enlighten us?
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