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bdg

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投稿

Agent Index Documenting Technical and Safety Features

arxiv.org
1 ポイント·投稿者 bdg·3 か月前·1 コメント

Generative artificial intelligence for computational chemistry

arxiv.org
3 ポイント·投稿者 bdg·3 か月前·0 コメント

What a 1GW Orbital GPU Farm Looks Like

orbital-dc.research.statagroup.com
4 ポイント·投稿者 bdg·5 か月前·0 コメント

コメント

bdg
·6 か月前·議論
I wonder if going the other way, maxing out semantic density per token, would improve LLM ability (perhaps even cost).

We use naturally evolved human languages for most of the training, and programming follows that logic to some degree, but what if the LLMs were working in a highly complex information dense company like Ithkuil? If it stumbles on BF, what happens with the other extreme?

Or was this result really about the sparse training data?