Learning by X-ray: When Surgical Robots Teach Themselves to See in Shadows
A grounded look at what imitation learning can—and cannot—do when surgical robots must plan spine trajectories from sparse bi-planar X-rays.
A grounded look at what imitation learning can—and cannot—do when surgical robots must plan spine trajectories from sparse bi-planar X-rays.
A mechanism-first reading of how institutional power, informal access, and personal judgement shape AI governance before formal rules catch up.
A practical reading of how noise augmentation can reduce chest X-ray generalization gaps, and why dataset composition still decides whether medical AI behaves.
OMPILOT shows why domain-specific AI for parallel programming is less about bigger models and more about constraining generation around the fragile semantics of shared-memory code.
A case-first look at PLLuM, Poland’s end-to-end sovereign AI stack for public administration, Polish-language alignment, governed data, RAG, and deployment-grade safety.
A sharper look at how Bayesian preference inference can make RLHF less wasteful by asking humans better questions, not simply asking them fewer.
AI governance will not scale through more policy PDFs; it needs structured memory that tracks rules, obligations, evidence, and failures across jurisdictions.
A mechanism-first look at LUME-DBN, a Bayesian framework that learns temporal clinical networks while treating missing ICU measurements as uncertainty to model, not debris to sweep away.
A practical reading of how nearest-neighbor retrieval improves differentially private in-context learning without pretending privacy engineering has become effortless.
MusRec shows how rectified-flow inversion and attention feature injection can turn pretrained music generators into practical zero-shot music editing engines.