Computer Vision - teaching computers to understand images - is a classically hard problem.
Why? Because computers don’t see images at all - just numbers where humans have had millions of years to become great at seeing.
So why are modern Machine Learning techniques such a big deal for CV? It is now clear that basic image understanding can be reduced to a fuzzy pattern matching exercise and that’s where our current ML is at. And modern ML is a massive step up from where CV was only a decade ago.
Two, the reams of video data we are producing provide both the incentive and the means to solve the problem. CV can add value to video data produced in industries such as security, manufacturing and medicine by processing it quickly and efficiently, much cheaper than people can. The same data can be used to train the ML models to process it.
The killer app for ML for CV? Edge processing of video. Managing privacy concerns inherent with video recordings, and bandwidth issues to stream video to the cloud from remote sites both mean that processing video at the edge, before sending results to the cloud is a massive win.
My prediction: it’s only going to get bigger.