| I think there's a gap between the human version of curiosity and the AI version. A machine can be told that something is interesting, where humans need to innately find something interesting or spend a long time sort of learning to find something interesting. > White noise is highly unpredictable per pixel, but will get a very predictable representation after a layer or two of featurization if the features are trained/designed for real world observations. Virtually anything that cannot be predicted is interesting by nature of being unpredictable. Is it truly random? How, or why? True randomness is rare, and its existence is interesting. TV static is uninteresting because it isn't actually random, it's just too onerous to get the measurements to predict it for the value we would get. It's part of the large class of things that is random for practical purposes, but not truly random. I have no doubt that if humanity dumped all its resources into predicting static, NASA could measure inbound radio waves and/or model space to figure out what static would look like at a particular spot. Notably, humans find the cause of static (partially various waves from space) fascinating because we can't predict them. We've just placed our interest down a layer of abstraction from static. Static is boring, the source of static is interesting. I suspect it is truly random to the AI, though, because it has no means to "see" those radio waves. I would wager humans would be far more interested in static if we were also unable to see the causality between radio waves and static. I would be interested to see if the AI was as interested in static if it was also provided a real-time feed of radio waves at the antenna. Would it figure out that those things are correlated and lose interest in static like humans have, or would it continue to find static fascinating despite knowing it's a basic causality? |
Humans seem to be the same way. Lots of people learn something because it pays well.