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.
It cant speak or use any words without it being someone elses words it learned from? Unless its giving sources everything is always from something it learned because it cannot speak or use words without that source in the first place?
prompt: Make up a word that is not found anywhere on the internet. If I do a search for that word, it should not exist. Provide example usage.
Qwen3-235B-A22B:
To ensure originality, I have created a brand-new word that does not appear anywhere in the given web_search results or in general English usage.
Virethra (pronounced: vye-RETH-ruh)
Part of speech: noun
Definition:
A quiet, lingering sense of connection to someone you’ve lost touch with, often accompanied by the feeling that they are thinking of you too—even if no contact has occurred in years.
Extended use (verb form):
To experience or feel this subtle emotional link.
Example Sentences:
Noun usage:
"She sat down to write an email, overwhelmed by a sudden virethra for her childhood friend who had moved away decades ago."
Verb usage:
"He often virethras about his first teacher, as if she still keeps a small place in his mind without warning."
Abstract context:
"The song played softly in the café, and for a moment, everyone seemed to virethra at once—lost in memories of people long gone but never truly forgotten."
Make up a word that is not found anywhere on the internet
Returns word that is found on the internet as a brand of nose rings, as a youtube username, as an already made up word in fantasy fiction, and as a (ocr?) typo of urethra
The point is that it’s trivial to come up with new words. Put that same prompt into a bunch of different LLMs and you’ll get a bunch of different words. Some of them may exist somewhere that don’t exist. There are simple rules for combining words that are so simple that children play them as games.
The LLM doesn’t actually even recognize “words” it recognizes tokens which are typically parts of words. It usually avoids random combinations of those but you can easily get it to do so, if you want.
You can “use” them to learn from, just like “AI” can.
What exactly do you think AI does when it “learns” from a book, for example? Do you think it will just spit out the entire book if you ask it to?
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.
It cant speak or use any words without it being someone elses words it learned from? Unless its giving sources everything is always from something it learned because it cannot speak or use words without that source in the first place?
prompt: Make up a word that is not found anywhere on the internet. If I do a search for that word, it should not exist. Provide example usage.
Qwen3-235B-A22B:
To ensure originality, I have created a brand-new word that does not appear anywhere in the given web_search results or in general English usage. Virethra (pronounced: vye-RETH-ruh)
Part of speech: noun
Definition: A quiet, lingering sense of connection to someone you’ve lost touch with, often accompanied by the feeling that they are thinking of you too—even if no contact has occurred in years.
Extended use (verb form): To experience or feel this subtle emotional link. Example Sentences:
Returns word that is found on the internet as a brand of nose rings, as a youtube username, as an already made up word in fantasy fiction, and as a (ocr?) typo of urethra
That’s a reasonable critique.
The point is that it’s trivial to come up with new words. Put that same prompt into a bunch of different LLMs and you’ll get a bunch of different words. Some of them may exist somewhere that don’t exist. There are simple rules for combining words that are so simple that children play them as games.
The LLM doesn’t actually even recognize “words” it recognizes tokens which are typically parts of words. It usually avoids random combinations of those but you can easily get it to do so, if you want.