How reinforcement learning controls tokamak plasma: the magnetic-control problem, the RL controller architecture, sim-to-real transfer, safety interlocks and what still limits it.
Neural decoding explained: how a brain-computer interface turns spikes into intent - signal chain, spike sorting, decoder models, closed-loop calibration and honest limits.
AI retrosynthesis explained: how template-based, transformer and MCTS search systems plan synthetic routes backward from a target molecule - reaction prediction, scoring, and honest limits in 2026.
Neural operators explained: how Fourier Neural Operators and DeepONet learn PDE solution maps for millisecond physics simulation - spectral convolution, discretization invariance, training and honest limits in 2026.
Single-cell foundation models explained: how scGPT, Geneformer and scFoundation pretrain transformers on tens of millions of cells for cell typing, perturbation and gene networks - architecture, benchmarks and caveats in 2026.
How AI weather forecasting models work: GraphCast graph neural networks, GenCast diffusion ensembles and Microsoft Aurora - encoder-processor-decoder design, training on reanalysis, skill vs ECMWF and honest limits in 2026.
How space debris tracking actually works: radar and optical sensor networks, orbit determination, covariance, probability-of-collision screening, conjunction data messages and automated collision avoidance in 2026.
How connectomics maps the brain: serial electron microscopy, AI segmentation, the fly and mouse connectomes, and why wiring diagrams are reshaping neuroscience.
How radar actually works: transmitting pulses, echo timing and range, the Doppler shift for velocity, phased-array beam steering, and why stealth shapes matter.