I am educated on this. When an ai learns, it takes an input through a series of functions and are joined at the output. The set of functions that produce the best output have their functions developed further. Individuals do not process information like that. With poor exploration and biasing, the output of an AI model could look identical to its input. It did not “learn” anymore than a downloaded video ran through a compression algorithm.
I am not sure what your contention, or gotcha, is with the comment above but they are quite correct. And additionally chose quite an apt example with video compression since in most ways current ‘AI’ effectively functions as a compression algorithm, just for our language corpora instead of video.
Video compression developers go through a lot of effort to make them deterministic. We don’t necessarily care that a particular video stream compresses to a particular bit sequence but we very much care that the resulting decompression gets you as close to the original as possible.
AIs will rarely produce exact replicas of anything. They synthesize outputs from heterogeneous training data. That sounds like learning to me.
The one area where there’s some similarity is dimensionality reduction. Its technically a form of compression, since it makes your files smaller. It would also be an extremely expensive way to get extremely bad compression. It would take orders of magnitude more hardware resources and the images are likely to be unrecognizable.
I am educated on this. When an ai learns, it takes an input through a series of functions and are joined at the output. The set of functions that produce the best output have their functions developed further. Individuals do not process information like that. With poor exploration and biasing, the output of an AI model could look identical to its input. It did not “learn” anymore than a downloaded video ran through a compression algorithm.
You are obviously not educated on this.
I am not sure what your contention, or gotcha, is with the comment above but they are quite correct. And additionally chose quite an apt example with video compression since in most ways current ‘AI’ effectively functions as a compression algorithm, just for our language corpora instead of video.
They seem pretty different to me.
Video compression developers go through a lot of effort to make them deterministic. We don’t necessarily care that a particular video stream compresses to a particular bit sequence but we very much care that the resulting decompression gets you as close to the original as possible.
AIs will rarely produce exact replicas of anything. They synthesize outputs from heterogeneous training data. That sounds like learning to me.
The one area where there’s some similarity is dimensionality reduction. Its technically a form of compression, since it makes your files smaller. It would also be an extremely expensive way to get extremely bad compression. It would take orders of magnitude more hardware resources and the images are likely to be unrecognizable.