computer-vision

How AI detects patterns in medical scans: convolutional networks learn from labeled images, what trials show, and why FDA-cleared tools still need clinicians.

LiDAR fires millions of laser pulses per second to build 3D point clouds. It powers self-driving cars, iPhones, and the discovery of lost Maya cities.

How computer vision AI works: from handcrafted features to AlexNet, CNNs, ResNet, Vision Transformers, CLIP, and where machine vision still fails.

How multimodal AI turns images, audio, and video into tokens a language model can process, what benchmarks show, and where vision-language models still fail.

In 2010 the best image classifier missed the right label in its top five guesses about 28% of the time. By 2015 that was 3.57%. Here is what changed, and how a CNN actually sees.

Object detection went from slow two-stage pipelines to real-time single-pass networks and transformer detectors. Here is how each generation works and what the benchmark numbers mean.

Neural style transfer lets AI blend the content of one image with the style of another. The results are stunning — and philosophically thorny.

Neural networks now find vessels in satellite radar images and recognize whale calls in underwater audio. Here is what they do, how accurate they are, and where the claims run ahead of the evidence.

Self-driving cars combine cameras, radar, lidar, and AI into one of the most complex robots ever built. Here's how the sensors, software, and decisions actually work.