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by k_sh 2856 days ago
The AI sees data/markers/patterns that look like something it's seen before, as opposed to actually comprehending that it sees a tube of meat that people call a hot dog.

The best metaphor I can think of is the cognitive difference between navigating a transit station that has signs in your native language, and one that you spent a couple of hours learning on Duolingo - with the latter, you aren't really understanding anything, just associating a:b::x:y.
3 comments
shawn 2856 days ago
This might be another formulation of the "Chinese Room" argument: https://en.wikipedia.org/wiki/Chinese_room

If every action is the same -- that is, if you produce some actions which would have been produced if you "conceptualized" it rather than merely "memorized" it -- isn't that identical?

The only thing we can do in life is make decisions. Regardless of how they're derived, if those decisions are identical to yours, isn't that entity "you" in some sense?

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schiffern 2856 days ago
>if those decisions are identical to yours, isn't that entity "you" in some sense?

If by "decisions" you mean every single nerve impulse in response to every possible set of stimuli, then that's pretty exacting. Every wobble while standing, every mouth movement answering any possible question, etc.

Also, how do you determine if the responses are "identical?" It's not like we can rewind reality and play it back, substituting yourself for an AI. And due to quantum nondeterminism, even if you played it back with no substitution your actions will diverge over time! If you're not considered identical to yourself, how is that a useful definition/test of "identicality"?

At the required fidelity, this thought-experiment is problematic both in theory and in practice. It obscures more than it illuminates imo.

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ben_w 2856 days ago
Transit is probably not extreme enough because a:b::x:y is fine.

Figures of speech are probably a better example (at least if translated literally, Duolingo teaches the equivalent phrases and is therefore easy to forget it doesn’t teach the meaning).

“Der Tropfen, der das Fass zum Überlaufen brachte”. What is the origin story that makes the English equivalent about camels, anyway?

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red75prime 2856 days ago
Anyone can try to step into neural network shoes on https://rach0012.github.io/humanRL_website/

It is still much easier for us, because we just need to connect new data to existing concepts.

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