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Yes this is more like compression to remember and not for learning/understanding.
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Compression is the reason why these Models are able to learn and understand.

My brain is doing the exact same thing.

I learned enough to compress concepts like a bike and what a bike does and for what i can use a bike.

Ask a LLM and it will answer you similiar to humans.

Blind people learn concepts of bikes too and in a smiliar way: by description.

LLMs just have so much data in form of text available and are able to ingest all of this, that the LLM compression algorithm doesn't has to be that good/finetuned than ours.

But I would assume that Yann LeCun's JEPA or other breakthroughs in the next few years will get us there.

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> Blind people learn concepts of bikes too and in a smiliar way: by description.

And by touch and sound. And maybe some were daring enough to drive one, or unlucky enough to get hit by one. But have way more input than just texts.

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LLMs also have other inputs, like audio and images. They get encoded (just like a human eye encodes an image) and passed to the weights.
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Invisibilia's episode was my first exposure to it.

https://www.npr.org/programs/invisibilia/378577902/how-to-be...

The man posits that clicking is instinctual for blind people but they are told to quiet down in class and most never develop their echolocation abilities

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Wow. Thank you.
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So a blind person only can describe lava to you after they touched and heared it?
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A blind person has touched warm and hot things and gotten burned before, and then they are told lava is this molten liquid that is even hotter than anything they have touched. That is enough for them to understand.

A blind person that never touched a hot object wouldn't really know though, there is a reason we dismiss talk from people who lack experience.

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You don't know that. Yo don't know what someone would think if you tell them the general concept of cold and warm.

The reaction you should have, the feeling etc.

I asked chatgpt how it would describe a scene without mentioning temperature. It was very good in describing what a human would describe.

I'm aware of the bias we have against LLMs but I think people just underestimate how much data is there.

I'm not saying a robot wouldn't be better with this information or an LLM and they actually use temperature sensors for robots so they can control movement speed and dexterity with overheating elements but the gap is small.

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