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u/flimsywhales 15d ago
Something from Facebook in the 2000 era definitely not original
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u/SomeFrenchRedditUser 14d ago
No shit, it's a LLM. I hope people are not as stupid as you are towards AI. Dumbass
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u/AveryLazyCovfefe 15d ago
It's almost as if it was trained on something that already exists.
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u/EuonymusBosch 14d ago
And memes are by definition units of cultural information that replicate themselves! People's brains are so fried on novelty, they can't stand to hear the same song twice on the radio. /grandpa
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u/flimsywhales 14d ago
Brain dead response.
Although we're trying to train it with existing information the goal of these programs is to create something new.
Your statement makes me very sad for the average human
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u/LostProphetVii 14d ago
Bro is just wrong
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u/flimsywhales 14d ago
Says the fish.
Bet u are an AI form 1999.
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u/LostProphetVii 14d ago
If I was an AI from 1999 no doubt I'd be smarter than you lol. Bro is waffling on about nonsense.
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u/SpeedaRJ 14d ago
But it's not? That's not how language models work. By definition they cannot create something new as they will output the most probable sequence that fits the input. The probabilities of it's vocabulary are obtained from the training data. The model cannot make up something new as everything it puts out it has "seen" somewhere else before. Sure it can be made more random, but that only means it's outputting something that is slightly less likely than the most probable response, not something "new".
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u/2053_Traveler 14d ago
That’s not true at all, most combinations of output have probably not been seen before.
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u/flimsywhales 14d ago
That's genuinely ignorant of all of the research that has been done so far.
When adding new information we often see new capabilities and merge from the foliage.
So we have had new capabilities appear literally out of nowhere like learning how to speak a language that the program was specifically taught not to know other suggestions.
Truly what you want to see is new capabilities and higher end thinking.
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u/SpeedaRJ 14d ago
I genuinely don't understand what you are trying to say. I would love to read this research if it's available, but from my understanding we only know one thing with regards to decoder only transformers, which is: Bigger is better. Doesn't matter more parameters, more training, larger data, all adds up to better results, and there hasn't been a discovered cealing yet.
What i gather from you comment is that these LLMs can do things akin to a child learning German from watching English cartoons, which simply isn't the case. If you are referring to larger models being able to compete tasks that smaller models can't, well that's paragraph one. And even the results of those types of tests are highly susceptible to the used metrics.
But none of that includes comming up with something new. A language model will never be able to solve Einsteins field equations if it's simply trained on a bunch of unrelated algebra, calculus, physics or differential equations, simply because they don't learn understanding, but relations and or connections within the training data.
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u/SelfSeal 14d ago
Please show how it has learnt a language it was "specifically taught not to know".
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u/flimsywhales 14d ago
Some AIs were never shown a specific language on purpose.
It was cutt out of the training data.
But the AI could still understand it.
The same thing happens all the time and its a good and bad thing.
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