AI and the World's Hard Problems ─ Physics
Science and scholarship advanced by AI, by discipline
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Photonic circuits become trainable AI hardware
The authors demonstrate a physics-based in situ learning method that directly measures optical gradients in photonic integrated circuits. It allows diverse circuit topologies to be trained after fabrication, without requiring a full digital model of the circuit. The hardware is reported to achieve matrix design, multiwavelength computation and rapid meta-learning.
This light-powered AI can spot deepfakes with nearly 98% accuracy
Researchers at UCLA built an optical-neural processor that uses light to detect deepfake videos, aiming to screen the growing volume of AI-generated video. Unlike conventional systems that examine videos one by one, it analyzes 15 or more video streams at once during a single optical pass. The team reports nearly 98% detection accuracy, and the study was published in eLight.
Claude-shaped science
Prof. Matthew Schwartz describes how, after he stopped fighting Claude and let it find problems suited to its current capabilities, he built BootLoops, a toolkit for exact calculations in quantitative science. Because similar calculations recur across fields, Claude found connections to ecology, population genetics, and a dozen other areas, though these were often technically correct but initially unremarkable. Schwartz then worked with domain experts to steer BootLoops toward questions those fields care about.
Towards foundation-style models for energy-frontier heterogeneous neutrino detectors via self-supervised pretraining
Event interpretation in energy-frontier neutrino detectors is impractical for conventional reconstruction and hard for supervised models when labelled data are scarce. The authors pretrain a sparse Vision Transformer with masked-autoencoder reconstruction and relational voxel-level objectives, then fine-tune it jointly on classification and regression. On simulated events from the proposed FASER calorimeter, pretraining improved flavour and charm-quark identification, momentum regression and vertex reconstruction, and with about 1,000 labelled events the pretrained encoder matched the flavour-classification performance of a model trained from scratch.
Claude computes a nine-loop amplitude in N=4 super-Yang-Mills
Physicist and science writer Matt von Hippel challenged AI companies to solve a hard theoretical physics problem: computing a nine-loop amplitude in N=4 super-Yang-Mills. Claude computed it, beating the challenge about a month later.