WIRED recently described an iPad app for basketball coaches called HomeCourt. You don’t have to be a pro to use it; is as easy as pointing an iPad’s camera at action on the court. Then the tricky stuff happens automatically. HomeCourt uses the support for machine learning added Apple’s mobile operating system last year to analyze the video. The app tracks each time a player shoots, scores, or misses, and logs the shooter’s location on the court. Each event is indexed so a particular play can later be viewed with a single tap.
HomeCourt is built on tools announced by Apple last summer, when they launched their bid to become a preferred playground for AI-curious developers. Known as Core ML, those tools help developers who’ve trained machine learning algorithms deploy them on Apple’s mobile devices and PCs.
Apple is far from the first tech company to release software to help developers build machine learning models. Facebook, Amazon, Microsoft, and Google have all done so, with TensorFlow the most popular. Apple claims none easily fit into an app developer’s regular workflow, limiting machine learning’s potential. I will review these different approached at my upcoming artificial intelligence and deep learning programs by Terrapinn Training, GLDNAcademy and GLC Europe.