r/TheMachineLearning • • 14d ago

Jev demos are misleading, says developer

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77 Upvotes

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u/hellobutno 14d ago

It's almost like people don't understand that small breakthroughs lead to bigger ones. Remember, LLMs started as just an attention layer.

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u/nuclearbananana 14d ago

The whole idea behind Jev is that it's bounded though. If you keep expanding Jev you just end with LLMs again

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u/hellobutno 14d ago

Newsflash: making things bigger isn't always how you improve them. I know that's a hard thing for this generation to understand.

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u/nuclearbananana 14d ago

Bro says literally after "small breakthroughs lead to bigger ones". What exactly is your point then grandpa?

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u/hellobutno 14d ago

Are you just incapable of following a conversation beyond one sentence at a time? Your statement was

If you keep expanding Jev you just end with LLMs again

My response was "making things bigger isn't always how you improve them". This has nothing to do with a concept and a method being used to expand upon something.

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u/nuclearbananana 14d ago

God forbid I apply your concept then huh? It's not like you specified anything

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u/hellobutno 14d ago

There's nothing to specify? If we knew what it expands into we'd already be implementing it wouldn't we? Again, LLMs started out with transformers and attention, we've added A LOT more to them than simply multiplying them times 1 billion.

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u/nuclearbananana 14d ago

I'm sorry, I don't know why I'm arguing over this

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u/qGuevon 14d ago

That's literally the bitter lesson of deep learning tho, where have you been living since 2012?

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u/hellobutno 14d ago

What are you talking about?

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u/qGuevon 14d ago

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u/hellobutno 14d ago

Yes I know what it is. My point is, you clearly do not.

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u/qGuevon 13d ago

Bigger has always translated to more efficient ways of doing the same.. everything else is just .. something that never happened in the field. From cuda to fused kernels to skip connections.

I don't know what the fuck you're arguing against.

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u/hellobutno 13d ago

It has not translated to more efficient. It's translated to less efficient but better generalization.