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> I don't know of any optical illusions where a bus turned upside down is perceived as a plowshare, or an image of an elephant superimposed on the image of a ro

> I don't know of any optical illusions where a bus turned upside down is perceived as a plowshare, or an image of an elephant superimposed on the image of a room causes the other objects in the room to be misidentified. I wouldn't be so sure about that actually. Observing my daughters, I'm sometimes extremely surprised about how they identify objects. As you grow, your concious mind probably simply does not notice the continuous cross-validation happening. Also, at the severe augmentation levels of years of ~50 fps "live video" training data, I can imagine there's another compensation algorithm at work in humans that tries to logically orient any picture into a most common, "upright" fashion before even trying to identify anything.

YeGoblynQueenne 8y ago
I don't understand what you mean by cross-validation. I'm guessing you don't mean actual cross-validation, where you test a model learned on a training partition against a testing partition, etc?
- tabtab 8y ago
  Maybe it's about context. One wouldn't expect to see a (realistic) elephant in a living room. For humans, if something seems "out of place", we tend to focus on it more to make sure we are interpreting it correctly. NN's have no sense of "out of place" other than "knowing" X and Y are not normally seen together in the training set. Humans give oddities "more CPU" (or run like heck to avoid being trampled and worry later about being right). We apply life experience and logic to explore it deeper. In the elephant case, the shadows look wrong also, which should trigger the "Maybe it's a cheap Photoshop job" module in our brain. But NN's don't understand the concept of cheap Photoshop jobs because they don't browse the web for cute kitten videos and fun but fake news like humans do. Maybe NN's can be trained for fake photo detection, but solving every identification hurdle of the human world by adding yet another dimension to the training step probably won't scale.

YeGoblynQueenne 8y ago

I don't understand what you mean by cross-validation. I'm guessing you don't mean actual cross-validation, where you test a model learned on a training partition against a testing partition, etc?

tabtab 8y ago
Maybe it's about context. One wouldn't expect to see a (realistic) elephant in a living room. For humans, if something seems "out of place", we tend to focus on it more to make sure we are interpreting it correctly. NN's have no sense of "out of place" other than "knowing" X and Y are not normally seen together in the training set. Humans give oddities "more CPU" (or run like heck to avoid being trampled and worry later about being right). We apply life experience and logic to explore it deeper. In the elephant case, the shadows look wrong also, which should trigger the "Maybe it's a cheap Photoshop job" module in our brain. But NN's don't understand the concept of cheap Photoshop jobs because they don't browse the web for cute kitten videos and fun but fake news like humans do. Maybe NN's can be trained for fake photo detection, but solving every identification hurdle of the human world by adding yet another dimension to the training step probably won't scale.