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The important counterweight to this phenomenon is the brain's adaptability to noise. Some ML researchers like to think that noise is not predictable because thi
by wantsanagent 3y ago
The important counterweight to this phenomenon is the brain's adaptability to noise. Some ML researchers like to think that noise is not predictable because this follows from the classic CS definition of noise. However in reality the brain quickly adapts to any noisy sensory input, it begins to predict higher level characteristics of the noisy input and no longer reacts with surprise or interest.
This happens at all levels of sensory processing, from single cell firing (which is noisy) to the boredom you feel with a 100 channels of TV that are all technically novel to you but contain nothing remotely interesting.
Basically if you've built an agent that can be perpetually distracted by noise or a "noisy" TV then you've forgotten an important piece of the puzzle.
- sdl 3y agoI like the Bayesian Surprise definition for this. It's not about predicting the exact next state of the world (or the next frame of the noisy TV) but about how much the next state changes your model of the world. https://papers.nips.cc/paper_files/paper/2005/hash/0172d289da48c48de8c5ebf3de9f7ee1-Abstract.html https://papers.nips.cc/paper_files/paper/2005/hash/0172d289d...