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.
Google search results aren’t deterministic but I wouldn’t say it “learns” like a person. Algorithms with pattern detection isn’t the same as human learning.
You may be correct but we don’t really know how humans learn.
There’s a ton of research on it and a lot of theories but no clear answers.
There’s general agreement that the brain is a bunch of neurons; there are no convincing ideas on how consciousness arises from that mass of neurons.
The brain also has a bunch of chemicals that affect neural processing; there are no convincing ideas on how that gets you consciousness either.
We modeled perceptrons after neurons and we’ve been working to make them more like neurons. They don’t have any obvious capabilities that perceptrons don’t have.
That’s the big problem with any claim that “AI doesn’t do X like a person”; since we don’t know how people do it we can neither verify nor refute that claim.
There’s more to AI than just being non-deterministic. Anything that’s too deterministic definitely isn’t an intelligence though; natural or artificial. Video compression algorithms are definitely very far removed from AI.
One point I would refute here is determinism. AI models are, by default, deterministic. They are made from deterministic parts and “any combination of deterministic components will result in a deterministic system”. Randomness has to be externally injected into e.g. current LLMs to produce ‘non-deterministic’ output.
There is the notable exception of newer models like ChatGPT4 which seemingly produces non-deterministic outputs (i.e. give it the same sentence and it produces different outputs even with its temperature set to 0) - but my understanding is this is due to floating point number inaccuracies which lead to different token selection and thus a function of our current processor architectures and not inherent in the model itself.
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.
Google search results aren’t deterministic but I wouldn’t say it “learns” like a person. Algorithms with pattern detection isn’t the same as human learning.
You may be correct but we don’t really know how humans learn.
There’s a ton of research on it and a lot of theories but no clear answers.
There’s general agreement that the brain is a bunch of neurons; there are no convincing ideas on how consciousness arises from that mass of neurons.
The brain also has a bunch of chemicals that affect neural processing; there are no convincing ideas on how that gets you consciousness either.
We modeled perceptrons after neurons and we’ve been working to make them more like neurons. They don’t have any obvious capabilities that perceptrons don’t have.
That’s the big problem with any claim that “AI doesn’t do X like a person”; since we don’t know how people do it we can neither verify nor refute that claim.
There’s more to AI than just being non-deterministic. Anything that’s too deterministic definitely isn’t an intelligence though; natural or artificial. Video compression algorithms are definitely very far removed from AI.
One point I would refute here is determinism. AI models are, by default, deterministic. They are made from deterministic parts and “any combination of deterministic components will result in a deterministic system”. Randomness has to be externally injected into e.g. current LLMs to produce ‘non-deterministic’ output.
There is the notable exception of newer models like ChatGPT4 which seemingly produces non-deterministic outputs (i.e. give it the same sentence and it produces different outputs even with its temperature set to 0) - but my understanding is this is due to floating point number inaccuracies which lead to different token selection and thus a function of our current processor architectures and not inherent in the model itself.