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Jacob X. Li discusses the need for AI systems to develop expertise autonomously from a corpus of documents, framing this as a challenging form of continual learning.
A twitter thread by @DSPyOSS and Jacob X. Li contrasts Machine Learning (optimizing from data with a precise objective) with 'Machine Studying' (learning from a declarative corpus without a downstream task), highlighting the urgent need for AI systems to develop expertise from unstructured documents.
Introduces 'Machine Studying' as a new formulation of continual learning where AI systems autonomously develop expertise from a corpus, and presents StudyBench for evaluation.
Introduces 'Machine Studying' as a problem where AI agents must autonomously develop expertise from a corpus, beyond RAG or long-context, and presents the StudyBench benchmark for evaluation.
Introduces the concept of 'Machine Studying' as a problem of developing expertise from a corpus of documents, distinct from continual learning.
Jacob Li introduces 'Machine Studying' as a new problem in continual learning: how AI systems develop expertise in an unfamiliar domain given only a corpus of documents, distinct from avoiding catastrophic forgetting.
Jacob Li introduces the concept of 'Machine Studying' as a distinct and urgent form of continual learning, where AI systems must develop expertise in a new domain from a corpus of documents alone.