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    AI Is Unsafe Until It Learns to Stabilize

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    Structured Reasoning as Equilibrium

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    Understanding Backpropagation, the Core Neural Network Algorithm

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Three Questions: Building a Two-Way Bridge Between AI and the Mathematical and Physical Sciences

How Diffusion Models Work
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MIT has released a white paper charting a “two-way bridge” between artificial intelligence and the mathematical and physical sciences, arguing that advances in each can accelerate the other. Chaired by physics professor Jesse Thaler, the effort follows a 2025 MIT workshop and calls for coordinated investment in computing and data infrastructure, cross-disciplinary research, and rigorous workforce training. The report promotes cultivating “centaur scientists” who are fluent in both domain science and AI, and highlights MIT efforts including the NSF-backed IAIFI, the A3D3 Institute, and university-wide initiatives in generative AI. MIT is moving toward structural changes—joint faculty lines, new degree pathways, and dedicated “science of AI” funding—to position the institute to lead as AI tools reshape discovery in physics, chemistry, mathematics, astronomy, and materials science.

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