MIT and DeepMind's new AI can't be tricked with weird lighting

Machine Vision has come a long way since Imagenet, a large repository of labelled images that researchers use to train their newest Artificial Intelligence (AI) agents with, was released, but still to this day images with bad, tricky or just plain weird lighting can still confuse even the best AI's algorithms and get them to misreport whatever it is they're looking at. And there are a multitude of examples where an AI has been tricked or confused, such as an AI that was being used to run an autonomous train prototype that mistook a shadow for a rock and came to a dead stop on the track, and even Nvidia's DAVE 2.0 self-driving car software that under certain lighting conditions would send a simulated car off a cliff, both of which exemplify the issue that machine vision enthusiasts everywhere still face. Over the past couple of years in order to try to overcome the issue researchers have either tried to create special hand crafted rules about how light interacts with objects or used data sets that cover as many lighting situations as possible, but there is a nearly limitless combination of items and light in the real world and that handicaps both approaches. Now though a paper by researchers from MIT and DeepMind has detailed a new AI process that can identify images in different lighting without having to hand craft new rules or train on a huge data set. The process, called a Rendered Intrinsics Network, or RIN for short, automatically separates an image into reflectance, shape, and lighting layers.