On October 12, the Royal Swedish Academy of Sciences announced that Robert E. Hall and Michael Woodford have been awarded the Nobel Prize in Economic Sciences. They are recognized for developing new macroeconomic models that significantly enhance the ability to implement effective stabilization policies. Based on these models, timely fiscal and monetary policy measures can reduce the peaks and troughs of the economy. Robert E. Hall and Michael Woodford received this year's award for demonstrating how to reconstruct economic models to revitalize stabilization policies. Instead of building models based on statistical relationships among macroeconomic variables such as inflation, production, and consumption, they opted to establish a foundation based on microeconomic principles. This includes understanding how the overall behavior of households and businesses influences the economy. Robert E. Hall was born in 1943 in Palo Alto, California, and earned his Ph.D. from the Massachusetts Institute of Technology in 1967. He is a professor at Stanford University in California. Michael Woodford was born in 1955 in Chicopee, Massachusetts, and earned his Ph.D. from the Massachusetts Institute of Technology in 1983. He is a professor at Columbia University in New York.
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2026 Nobel Prize in Economic Sciences Announced
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On October 12, according to an arXiv paper, researchers Wancong Zhang, Basile Terver, Michael Rabbat, Yann LeCun, and Randall Balestriero proposed H-JEPA, an end-to-end training scheme for a hierarchical action-conditioned JEPA world model, where each layer predicts further into the future within its learned latent space. Planning is conducted top-down: the top layer optimizes the goal, and each layer's predictions become sub-goals for the layer below. When factors in the data evolve at separate time scales, higher layers discard rapid, unpredictable details while retaining slower, task-relevant states. In four simulated navigation and manipulation environments, hierarchical planning outperformed flat JEPA; in the Visual AntMaze task, a three-layer structure increased the success rate from 18% to 73%, while requiring less computational power for planning. Ablation experiments indicate that the gains come from both temporal decomposition and high-level goal representation. Under inverse dynamics supervision, this method can be scaled to real robot videos in DROID. -
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Amazon Plans $7 Billion Acquisition of AI Company Decart
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