Big Learning: A Universal Machine Learning Paradigm?
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Recent breakthroughs based on big/foundation models reveal a vague avenue for artificial intelligence, that is, bid data, big/foundation models, big learning, ⋯. Following that avenue, here we elaborate on the newly introduced big learning. Specifically, big learning comprehensively exploits the available information inherent in large-scale complete/incomplete data, by simultaneously learning to model many-to-all joint/conditional/marginal data distributions (thus named big learning) with one universal foundation model. We reveal that big learning is what existing foundation models are implicitly doing; accordingly, our big learning provides high-level guidance for flexible design and improvements of foundation models, accelerating the true self-learning on the Internet. Besides, big learning (i) is equipped with marvelous flexibility for both training data and training-task customization; (ii) potentially delivers all joint/conditional/marginal data capabilities after training; (iii) significantly reduces the training-test gap with improved model generalization; and (iv) unifies conventional machine learning paradigms e.g. supervised learning, unsupervised learning, generative learning, etc. and enables their flexible cooperation, manifesting a universal learning paradigm.
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