Phuc Tran
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The paper proposes a novel, computationally efficient estimator for estimating heterogeneous treatment effects in panel data by framing the problem as matrix completion and establishing a sharp row-wise $\ell_2$ error bound.
The paper proposes viewing national AI development, specifically in France, as a 'national AI learning system' governed by a controlled balance between information injection and entropy dissipation, arguing that sovereignty depends on regulating this internal dynamic rather than just scale.
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AI Sovereignty as National Learning Capacity: A Human-Centered Learning Mechanics Viewpoint on France, the United States, and China
The paper proposes viewing national AI development, specifically in France, as a 'national AI learning system' governed by a controlled balance between information injection and entropy dissipation, a…