English breaks down here, but the model probably does "know" something more like "If the tree is here in this frame, in the next frame, it will be there, give o

English breaks down here, but the model probably does "know" something more like "If the tree is here in this frame, in the next frame, it will be there, give or take some waving in the wind". It doesn't know that "trees don't walk", just as it doesn't know that "trees don't levitate", "trees don't spontaneously turn into clowns", or an effectively infinite number of other things that trees don't do. What it can do possibly do is realize that in frame 1 there was a tree, and then in frame 2, there was something the model didn't predict as a high-probability output of the next frame. It isn't about knowing that trees don't walk, but that trees do behave in certain ways and noticing that it is "surprised" that they fail to behave in the predicted ways, where "surprise" is something like "this is a very low probability output of my model of the next frame". It isn't necessary to enumerate all the ways the next frame was low-probability, it is enough to observe that it was logically-not high probability. In a lot of cases this isn't necessarily that useful, but in a security context having a human take a look at a "very low probability series of video frames" will, if nothing else, teach the developers a lot about the real capability of the model. If it spits out a lot of false positives, that is itself very informative about what the model is "really" doing.