Primates' retinal ganglion cells receive visual info from photoreceptors that they then transmit from the eye to the brain. But not all cells are created equal -- an estimated 80% operate at low frequency and recognize fine details, while about 20% respond to swift changes. This biological dichotomy inspired scientists at Facebook AI Research to pursue what they call SlowFast. It's a machine learning architecture for video recognition that they claim achieves "strong performance" for both action classification and detection in footage. An implementation in Facebook's PyTorch framework -- PySlowFast -- is available on GitHub, along with trained models. As the research team points out in a preprint paper, slow motions occur statistically more often than fast motions, and the recognition of semantics like colors, textures, and lighting can be refreshed slowly without compromising accuracy.