  Recent advances in big/foundation models reveal a promising path for deep
learning, where the roadmap steadily moves from big data to big models to (the
newly-introduced) big learning. Specifically, the big learning exhaustively
exploits the information inherent in its large-scale complete/incomplete
training data, by simultaneously modeling many/all joint/conditional/marginal
data distributions across potentially diverse domains, with one universal
foundation model. We reveal that big learning ($i$) underlies most existing
foundation models, ($ii$) is equipped with extraordinary flexibilities for
complete/incomplete training data and trustworthy data tasks, ($iii$) is
capable of delivering all joint/conditional/marginal data capabilities with one
universal model, and ($iv$) unifies conventional machine learning paradigms and
enables their flexible cooperations, manifested as a universal learning
paradigm. Diverse experiments are carried out to validate the effectiveness of
the presented big learning.
